• 인간식물환경학회지
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• 제21권6호
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• pp.485-492
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• 2018

This study aimed to develop and validate highly sensitive determination method of a prostaglandin ($PGE_1$, OP 1206) in human plasma by LC-MS/MS using column switching. Plasma stored at $-30^{\circ}C$ and treated with methanol effectively inhibited interferences synthesized post-sampling. Samples were added with internal standard and were separated by reversed-phase HPLC with a cycle time of 30min. The method was selective for OP 1206 and the regression models, based on internal standard, were linear across the concentration range 0.5-50 pg/mL with the limit of quantification of 0.5 pg/mL (limit of quantitation, LOQ) for OP 1206. The calibration curve of OP 1206 standards spiked in five individual plasma samples was linear ($r^2=0.9999$). Accuracy and precision at the concentrations of 0.5, 1.5, 5.0 and 40 pg/mL, and at the lower LOQ of 0.5 pg/mL were excellent at 20%. OP120 < 6 was stable in plasma samples for at least 24 hours at room temperature, 24 hours frozen at $-70^{\circ}C$, 24 hours in an auto sampler at $6^{\circ}C$, and for two freeze/unfreezing cycles. The validated determination method successfully quantified the concentrations of OP 1206 in plasma samples from simulated administrating a single $5{\mu}g$ OP 1206 formulation. Thus, this novel LC-MS/MS technique for drug separation, detection and quantitation is expected to become the standard highly-sensitive detection method in bioanalysis and to be applied to many low dose pharmaceutical products.

• 스마트미디어저널
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• 제12권11호
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• pp.1-9
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• 2023

스트레스는 감당하기 어려운 외부 또는 내부 요인으로부터 유발되는 것으로 현대 사회의 주요한 문제 중 하나이다. 높은 스트레스가 장기적으로 지속되면 만성적으로 발전할 수 있으며, 건강 및 생활 전반에 큰 악영향을 초래할 수 있다. 그러나 만성적인 스트레스를 겪는 사람들은 자신이 스트레스를 받고 있는지 알아차리기 어렵기 때문에 사전에 스트레스를 인지하고 관리하는 것이 중요하다. 웨어러블 기기로부터 측정된 생체 신호를 이용하여 스트레스를 탐지한다면, 스트레스를 효율적으로 관리할 수 있을 것이다. 그러나 생체 신호를 이용하는 데에는 두 가지 문제점이 있다. 첫째로 생체 신호에서 수작업 특징을 추출하는 것은 바이어스를 발생시킬 수 있으며, 두 번째는 실험 주체에 따라 분류 모델 성능의 변이가 클 수 있다는 것이다. 본 논문에서는 데이터의 핵심적인 특징을 표현할 수 있는 합성곱 오토인코더를 이용해 바이어스를 줄이고 앙상블 학습 중 하나인 소프트 보팅을 이용해 일반화 능력을 높여 성능의 변이를 줄이는 모델을 제안한다. 모델의 일반화 성능을 확인하기 위하여 LOSO 교차 검증 방법을 이용하여 성능을 평가한다. 본 논문에서 제안한 모델은 WESAD 데이터셋을 이용하여 높은 성능을 보여주었던 기존의 연구들보다 우수한 정확도를 보임을 확인하였다.

• Current Optics and Photonics
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• 제4권5호
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• pp.428-433
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• 2020

Recently, the on-chip autofocus (AF) function has become essential to the CMOS image sensor. An auto-focus usually operates using phase detection of the photocurrent difference from a pair of AF pixels that have focused or defocused. However, the phase-detection method requires a pair of AF pixels for comparison of readout. Therefore, the pixel variation may reduce AF performance. In this paper, we propose a color-selective AF pixel with a plasmonic nanostructure in a 0.9 μ㎡ pixel. The suggested AF pixel requires one pixel for AF function. The plasmonic nanostructure uses metal-insulator-metal (MIM) stack arrays instead of a color filter (CF). The color filters are formed at the subwavelength, and they transmit the specific wavelength of light according to the stack period and incident angles. For the optical analysis of the pixel, a finite-difference time-domain (FDTD) simulation was conducted. The analysis showed that the MIM stack arrays in the pixels perform as an AF pixel. As the primary metric of AF performance, the resulting AF contrasts are 1.8 for the red pixels, 1.6 for green, and 1.5 blue. Based on the simulation results, we confirmed the autofocusing performance of the MIM stack arrays.

• 대한전기학회논문지:전력기술부문A
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• 제54권11호
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• pp.525-532
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• 2005

This paper presents a new two-terminal numerical algorithm for fault location estimation and for faults recognition using the synchronized phaser in time-domain. The proposed algorithm is also based on the synchronized voltage and current phasor measured from the assumed PMUs(Phasor Measurement Units) installed at both ends of the transmission lines. Also the arc voltage wave shape is modeled numerically on the basis of a great number of arc voltage records obtained by transient recorder. From the calculated arc voltage amplitude it can make a decision whether the fault is permanent or transient. In this paper the algorithm is given and estimated using DFT(discrete Fourier Transform) and the LES(Least Error Squares Method). The algorithm uses a very short data window and enables fast fault detection and classification for real-time transmission line protection. To test the validity of the proposed algorithm, the Electro-Magnetic Transient Program(EMTP/ATP) is used.

• Journal of Communications and Networks
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• 제11권3호
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• pp.248-260
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• 2009

To connect a mobile ad hoc network (MANET) with an IP network and to carryout communication, ad hoc network node needs to be configured with unique IP adress. Dynamic host configuration protocol (DHCP) server autoconfigure nodes in wired networks. However, this cannot be applied to ad hoc network without introducing some changes in auto configuration mechanism, due to intrinsic properties (i.e., multi-hop, dynamic, and distributed nature) of the network. In this paper, we propose a scalable autoconfiguration scheme for MANETs with hierarchical topology consisting of leader and member nodes, by considering the global Internet connectivity with minimum overhead. In our proposed scheme, a joining node selects one of the pre-configured nodes for its duplicate address detection (DAD) operation. We reduce overhead and make our scheme scalable by eliminating the broadcast of DAD messages in the network. We also propose the group leader election algorithm, which takes into account the resources, density, and position information of a node to select a new leader. Our simulation results show that our proposed scheme is effective to reduce the overhead and is scalable. Also, it is shown that the proposed scheme provides an efficient method to heal the network after partitioning and merging by enhancing the role of bordering nodes in the group.

• Journal of Animal Science and Technology
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• 제65권3호
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• pp.627-637
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• 2023

As the population and income levels rise, meat consumption steadily increases annually. However, the number of farms and farmers producing meat decrease during the same period, reducing meat sufficiency. Information and Communications Technology (ICT) has begun to be applied to reduce labor and production costs of livestock farms and improve productivity. This technology can be used for rapid pregnancy diagnosis of sows; the location and size of the gestation sacs of sows are directly related to the productivity of the farm. In this study, a system proposes to determine the number of gestation sacs of sows from ultrasound images. The system used the YOLOv7-E6E model, changing the activation function from sigmoid-weighted linear unit (SiLU) to a multi-activation function (SiLU + Mish). Also, the upsampling method was modified from nearest to bicubic to improve performance. The model trained with the original model using the original data achieved mean average precision of 86.3%. When the proposed multi-activation function, upsampling, and AutoAugment were applied, the performance improved by 0.3%, 0.9%, and 0.9%, respectively. When all three proposed methods were simultaneously applied, a significant performance improvement of 3.5% to 89.8% was achieved.

• 자동차안전학회지
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• 제5권1호
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• pp.44-49
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• 2013

This paper presents a lateral collision risk index between an ego vehicle and a rear-side vehicle. The lateral collision risk is designed to represent a lateral collision risk and provide the appropriate threshold value of activation of the lateral collision management system such as the Blind Spot Detection(BSD). The lateral collision risk index is designed using the Time to Line Crossing(TLC) and the longitudinal collision index at the predicted TLC. TLC and the longitudinal collision index are calculated with the signals from the exterior sensor such as the radar equipped on the rear-side of a vehicle and a vision sensor which detects the distance and time to the lane departure. For the robust situation assessment, the perception of driving environment determining whether the road is straighten or curved should be determined. The relative motion estimation method has been proposed with the road information via the integrated estimator using the environment sensors and vehicle sensor. A lateral collision risk index was composed with the estimated relative motion considering the relative yaw angle. The performance of the proposed lateral collision risk index is investigated via computer simulations conducted using the vehicle dynamics software CARSIM and Matlab/Simulink.

• 자동차안전학회지
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• 제14권2호
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권2호
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• pp.137-147
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• 2010

블로그는 웹과 로그의 합성어로, 개개인의 생각이나 관심사 등을 일기처럼 기록할 수 있는 웹 서비스이다. 블로그에는 문자 외에, 그림이나 비디오 파일 등 다양한 컨텐츠를 올릴 수 있다. 일반적으로 블로그의 포스트는 시간상의 역순으로 정렬되어 표현된다. 블로그 검색 엔진은 웹 검색 엔진처럼 블로그를 대상으로 사용자의 질의에 따라 정보를 찾아주는 서비스이다. 블로그 검색 엔진은 때때로 만족스럽지 못한 결과를 내곤 하는데, 이것은 스플로그라고 불리는 블로그 스팸에 의해 발생한다. 스플로그는 다른 블로그나 웹 페이지를 무단 도용하거나 자동으로 생성된 컨텐츠로 구성된 스팸 포스트를 가지고 있다. 스플로그는 검색 엔진의 검색 순위를 높이거나, 회원 가입 사이트로 보다 많은 사람들을 유치하기 위해 사용된다. 본 논문은 스플로그 탐지를 목적으로 한다. 본 논문에서 제안하는 스플로그 탐지 기법은 블로그 포스트의 구조 유사성과 일일 포스트 발행수에 따른 분석으로 토대로 이루어진다. 본 논문에서 제안하는 기법을 바탕으로 한 실험의 결과, 스플로그 탐지에 있어 90% 이상의 높은 정확도를 가지며, 만족할만한 수준을 보여준다.

• 전기전자학회논문지
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• 제24권2호
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• pp.475-485
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• 2020

최근, 별도의 센서를 설치하지 않고 센서가 포함된 사용자의 기기로부터 제공되는 실시간 센싱 데이터를 가지고 새로운 센싱 서비스를 제공하는 크라우드센싱(Crowdsensing) 시스템이 주목받고 있다. 크라우드센싱 시스템에서는 사용자의 조작실수나 통신 문제로 인해 의미 없는 데이터가 제공되거나 보상을 얻기 위해 거짓 데이터를 제공할 수 있어 해당 이상 데이터의 탐지 및 제거가 크라우드센싱 서비스의 질을 결정짓는다. 이러한 이상데이터를 탐지하기 위해 제안되었던 방법들은 크라우드센싱의 빠른 변화 환경에 효율적이지 않다. 본 논문은 머신러닝 기술을 활용하여 지속적이고 빠르게 변화하는 센싱 데이터의 특징을 추출하고 적절한 알고리즘을 통해 모델링하여 이상데이터를 탐지하는 방법을 제안한다. 지도학습의 딥러닝 이진 분류 모델과 비지도학습의 오토인코더 모델을 사용하여 제안 시스템의 성능 및 실현 가능성을 보인다.