• 대한토목학회논문집
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• 제31권2A호
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• pp.121-127
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• 2011

콘크리트 내부에 존재하는 공극(void)의 공간적 분포는 콘크리트의 역학적, 물리적 거동에 큰 영향을 미친다. 따라서 콘크리트 재료 물성의 파악과 건정성 평가를 위해 내부에 존재하는 공극의 분포 상태를 파악하는 것은 매우 중요하다. 콘크리트에는 육안으로 보이는 재료 표면의 공극 이외에도 내부 공극이 존재한다. 본 연구에서는 경량골재 콘크리트의 공극 분포를 파악하기 위하여 micro CT(X-ray microtomography)를 활용하여 생성된 3차원 콘크리트 디지털 시편을 사용하였다. 흑백처리된 단면 이미지를 중첩하여 공극을 묘사할 수 있는 3차원 시편을 생성하였다. 공극의 분포 상태를 확률적으로 묘사하기 위하여 확률 분포 함수 two-point correlation function과 lineal-path function으로 분석하였다. 또한, 이미지 분석을 통해서 콘크리트 시편의 공극의 밀도 분포를 파악하였다. 콘크리트 내부에 있는 개별 경량 골재의 공극도 이미지 처리와 확률 분포함수를 사용하여 분석하였다. Micro CT와 3차원 이미지 분석 방법을 통하여 콘크리트 내부에 존재하는 공극의 분포 상태를 효과적으로 파악할 수 있음을 확인하였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1034-1039
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• 2005

SALM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important tasks in mobile robot research. Until now expensive sensors such as a laser sensor have been used for mobile robot localization. Currently, the proliferation of RFID technology is advancing rapidly, while RFID reader devices, antennas and tags are becoming increasingly smaller and cheaper. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying location of the mobile robot in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, the localization error results from the sensing area of the RFID reader, because the reader just knows whether the tag is in the sensing range of the sensor and, until now, there is no study to estimate the heading of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. The Markov localization method is used to reduce the location(X,Y) error and the Kalman Filter method is used to estimate the heading($\theta$) of mobile robot. The algorithms which are based on Markov localization require high computing power, so we suggest fast Markov localization algorithm. Finally we applied these algorithms our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors such as odometers and RFID tags for mobile robot localization in the smart floor

• 한국생산제조학회지
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• 제20권4호
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• pp.412-418
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• 2011

Reliability-based Topology optimization(RBTO) is to get an optimal design satisfying uncertainties of design variables. Although RBTO based on homogenization and density distribution method has been done, RBTO based on BESO has not been reported yet. This study presents a reliability-based topology optimization(RBTO) using bi-directional evolutionary structural optimization(BESO). Topology optimization is formulated as volume minimization problem with probabilistic displacement constraint. Young's modulus, external load and thickness are considered as uncertain variables. In order to compute reliability index, four methods, i.e., RIA, PMA, SLSV and ADL(adaptive-loop), are used. Reliability-based topology optimization design process is conducted to obtain optimal topology satisfying allowable displacement and target reliability index with the above four methods, and then each result is compared with respect to numerical stability and computing time. The results of this study show that the RBTO based on BESO using the four methods can effectively be applied for topology optimization. And it was confirmed that DLSV and ADL had better numerical efficiency than SLSV. ADL and SLSV had better time cost than DLSV. Consequently, ADL method showed the best time efficiency and good numerical stability.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1797-1801
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• 2005

SLAM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important issues in mobile robot research. Until now expensive sensors like a laser sensor have been used for the mobile robot's localization. Currently, as the RFID reader devices like antennas and RFID tags become increasingly smaller and cheaper, the proliferation of RFID technology is advancing rapidly. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used to identify the mobile robot's location on the smart floor. We discuss a number of challenges related to this approach, such as RFID tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, because the reader just can senses whether a RFID tag is in its sensing area, the localization error occurs as much as the sensing area of the RFID reader. And, until now, there is no study to estimate the pose of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. We use the Markov localization algorithm to reduce the location(X,Y) error and the Kalman Filter algorithm to estimate the pose(q) of a mobile robot. We applied these algorithms in our experiment with our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors like odometers and RFID tags for the mobile robot's localization on the smart floor.

• 한국지진공학회논문집
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• 제22권2호
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• pp.45-54
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• 2018

The seismically isolated nuclear power plants shall be designed for design basis earthquake (DBE) and considered to ensure safety against beyond design basis earthquake (BDBE). In order to limit the excessive displacement of the seismic isolation system of the seismically isolated structure, the moat is installed at a certain distance from the upper mat supporting the superstructure. This certain distance is called clearance to stop (CS) and is calculated from the 90th percentile displacement of seismic isolation system subjected to BDBE. For design purposes, the CS can be obtained simply by multiplying the median displacement of the seismic isolation system against DBE by scale factor with a value of 3. The DBE and BDBE used in this study were generated by using 30 sets of artificial earthquakes corresponding to the nuclear standard design spectrum. In addition, latin hyper cube sampling was applied to generate 30 sets of artificial earthquakes corresponding to maximum - minimum spectra. For the DBE, the median displacement and the 99th percentile displacement of the seismic isolation system were calculated. For the BDBE, the suitability of the scale factor was assessed after calculating the 90th percentile displacement of the seismic isolation system.

• 한국통신학회논문지
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• 제34권4C호
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• pp.388-396
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• 2009

본 연구는 CCD카메라로부터 입력된 영상을 분석하여 특징값을 추출하고, 패턴인식기술을 이용하여 화재연기영상을 감지하는 방법을 제안한다. 우선 CCD카메라로부터 획득된 영상들간의 차영상을 이용하여 움직임 영역만을 검출하고, 이후 연기색상모델을 적용하여 후보영역을 생성한다. 연기영역은 유사색상의 군집화를 이루고, 주변에 비해 단순한 질감을 가지며, 시간에 따른 모션정보의 상승 방향성을 가지는 특징을 가진다. 본 논문에서는 연기영역의 이러한 특성을 이용하여 학습영상으로부터 연기의 밝기, 웨이블릿 고주파 성분, 모션 벡터 등의 특징 값을 추출하고 이들 특징 값들에 대해 가우시안 확률 모델을 생성한다. 이렇게 추출된 확률모델은 연기영역의 시간적 연속성을 고려하기 위해 본 논문에서 새롭게 구성한 동적 베이지안 네트워크의 관찰노드에 적용된다. 본 논문에서 제안하는 방법은 산불을 비롯한 다양한 연기를 감지하였으며, 기존의 알고리즘에 비해 우수한 성능을 보여주었다.

• Smart Structures and Systems
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• 제17권6호
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• 터널과지하공간
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• 제31권1호
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• pp.66-81
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• 2021

본 연구는 삼차원 절리텐서 파라미터와 DFN(discrete fracture network) 블록의 변형특성 간의 상관성 분석을 수행하여 절리텐서의 방향성분 및 일차불변량이 절리성 암반의 변형계수 및 전단탄성계수에 미치는 영향을 평가하였다. 확정적 방향성을 갖는 1~2개의 절리군을 사용하여 절리의 빈도 및 길이분포의 변화에 따라 생성한 총 224개의 DFN 블록에 대하여 절리텐서 파라미터가 산정되었다. 또한, 정육면체 DFN 블록에 대하여 개별요소법을 활용하여 서로 직교하는 세 방향으로 변형특성이 추정되었다. 절리텐서의 일차불변량이 증가할수록 변형계수 및 전단탄성계수는 대체로 저감되는 양상을 나타내지만, 감소폭이 줄어들어 일차불변량이 특정 기준값을 상회하면 변형계수 및 전단탄성계수는 거의 일정한 값을 유지하였다. 삼차원 DFN 블록에 대한 지향적 변형특성은 대응하는 방향의 절리텐서성분과 멱함수의 강한 상관관계를 도출하였다.

• Journal of Radiation Protection and Research
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• 제46권4호
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• pp.204-212
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• 2021

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of ²²Na, ⁶⁰Co, and ¹³⁷Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM > ANN > CNN, while the training time followed the order SVM > ANN > CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.127-139
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• 2023

Numerous genuine issues, for example, financial exchange expectation, climate determining and so forth has inalienable arbitrariness related with them. Receiving a probabilistic system for forecast can oblige this dubious connection among past and future. Commonly the interest is in the contingent likelihood thickness of the arbitrary variable included. One methodology for expectation is with time arrangement and auto relapse models. In this work, liner expectation technique and approach for computation of forecast coefficient are given and likelihood of blunder for various assessors is determined. The current methods all need in some regard assessing a boundary of some accepted arrangement. In this way, an elective methodology is proposed. The elective methodology is to gauge the restrictive thickness of the irregular variable included. The methodology proposed in this theory includes assessing the (discretized) restrictive thickness utilizing a Markovian definition when two arbitrary factors are genuinely needy, knowing the estimation of one of them allows us to improve gauge of the estimation of the other one. The restrictive thickness is assessed as the proportion of the two dimensional joint thickness to the one-dimensional thickness of irregular variable at whatever point the later is positive. Markov models are utilized in the issues of settling on an arrangement of choices and issue that have an innate transience that comprises of an interaction that unfurls on schedule on schedule. In the nonstop time Markov chain models the time stretches between two successive changes may likewise be a ceaseless irregular variable. The Markovian methodology is especially basic and quick for practically all classes of classes of issues requiring the assessment of contingent densities.