기존의 저수지 운영 연구들은 미래의 기후가 과거와 유사하다는 정상성의 가정을 전제로 하였다. 하지만 기후의 비정상성으로 인해 불확실성이 더욱 커질 경우에는 큰 불확실성에서도 안정된 최적해를 찾을 수 있는 로버스트 최적화 과정(Robust Optimization, 이하 RO)이 필요하다고 알려져 있다. RO는 입력자료의 비정상성으로 인해 야기되는 불확실성을 제어하는 로버스트 항을 목적함수에 추가하여 기존의 최적화 방법을 개선한다. 본 연구는 기후변화의 비정상성을 대비하는 저수지 운영규칙 산정을 위해 추계학적동적계획법(Stochastic Dynamic Programing, 이하 SDP)과 RO를 결합하는 Robust-SDP를 제안하였고, 이를 최근 4년간 가뭄을 겪었던 보령댐에 적용하였다. 즉, 비정상성이 반영된 미래 유입량 자료를 생성하고 이를 6가지의 평가지표와 2가지의 의사결정 지원그림을 사용하여 과거 유입량 자료로부터 산출된 저수지 운영규칙의 수행능력을 평가하 였다. 그 결과, Robust-SDP가 기후의 비정상성 하에서 극단적인 물 부족 사건의 발생률과 물 부족 사건의 실패의 크기를 감소시켰지만, 작은 크기의 물 부족 발생률은 증가하는 상충관계(trade-off)를 가져옴을 확인할 수 있었다. 이를 바탕으로 의사결정자가 우선시하는 평가지표의 결과에 따라 최적화 모형을 선택할 수 있음을 제안하였다.

Recently, the safety in vehicle also has become a hot topic as self-driving car is developed. In passive safety systems such as airbags and seat belts, the system is being changed into an active system that actively grasps the status and behavior of the passengers including the driver to mitigate the risk. Furthermore, it is expected that it will be possible to provide customized services such as seat deformation, air conditioning operation and D.W.D (Distraction While Driving) warning suitable for the passenger by using occupant information. In this paper, we propose robust vehicle occupant detection algorithm based on RGB-Depth-Thermal camera for obtaining the passengers information. The RGB-Depth-Thermal camera sensor system was configured to be robust against various environment. Also, one of the deep learning algorithms, OpenPose, was used for occupant detection. This algorithm is advantageous not only for RGB image but also for thermal image even using existing learned model. The algorithm will be supplemented to acquire high level information such as passenger attitude detection and face recognition mentioned in the introduction and provide customized active convenience service.

Magnoliae Flos (Sini in Korean) is one of the most important oriental medicinal plants. In the Korean Herbal Pharmacopeia, the bud of the all species in Manolia denudate and Manolia genus were regarded as the botanical sources for ‘Sini’. Most the dried bud of Manolia denudata, Manolia biondii and Manolia sprengeri were used as ‘Xin-yi’ in China. Therefore, the purpose of this study was to determine and compare the ‘Magnolia’ species, four species including Manolia denudata, M. biondii, M. liliiflora and M. Kobus were analysis of sequencing data revealed DNA polymorphisms. The based on tRNA coding leucine/phenylalanine (trnL-F) and NADH-plastoquinone oxidoreductase subunit 5 (ndhF) sequences in chloroplast DNA. For the identification of ‘Magnolia’ species, polymerase chain reaction (PCR) analysis of chloroplast DNA regions such as ndhF have proven an appropriate method. A single nucleotide polymorphism (SNP) has been identified between genuine “Sini” and their fraudulent and misuse. Specific PCR primers were designed from this polymorphic site within the sequence data, and were used to detect true plants via multiplex PCR.

We present a robust power transmission lines detection method based on vanishing point estimation. Vanishing point estimation can be helpful to detect power transmission lines because parallel lines converge on the vanishing point in a projected 2D image. However, it is not easy to estimate the vanishing point correctly in an image with complex background. Thus, we first propose a vanishing point estimation method on power transmission lines by using a probabilistic voting procedure based on intersection points of line segments. In images obtained by our system, power transmission lines are located in a fan-shaped area centered on this estimated vanishing point, and therefore we select the line segments that converge to the estimated vanishing point as candidate line segments for power transmission lines only in this fan-shaped area. Finally, we detect the power transmission lines from these candidate line segments. Experimental results show that the proposed method is robust to noise and efficient to detect power transmission lines.

Global localization is one of the essential issues for mobile robot navigation. In this study, an indoor global localization method is proposed which uses a Kinect sensor and a monocular upward-looking camera. The proposed method generates an environment map which consists of a grid map, a ceiling feature map from the upward-looking camera, and a spatial feature map obtained from the Kinect sensor. The method selects robot pose candidates using the spatial feature map and updates sample poses by particle filter based on the grid map. Localization success is determined by calculating the matching error from the ceiling feature map. In various experiments, the proposed method achieved a position accuracy of 0.12m and a position update speed of 10.4s, which is robust enough for real-world applications.

Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI) field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM) is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition. In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95% classification accuracy for three emotions: "joy", "anger", and "disgust".

기후변화에 따른 도시하천의 건천화 현상은 앞으로 더욱더 심각해질 것으로 예상되고 있다. 따라서 본 연구에서는 건천화의 적응전략으로서 하수처리수 재이용을 선정하였고 어느 지영기 우선적으로 선정되어야 하는가를 결정할 수 있는 방법을 제시하였다. 평가기준은 수문학적인 요소뿐만 아니라 인문·사회적인 요소도 포함하였다. 평가치와 가중치의 불확실성을 고려하기 위해 퍼지 이론을 사용하여서 기후변화로 인해 변화된 평가치와 평가기준에 대한 가중치의 불확실성을 완화하고자 하였다. Fuzzy TOPSIS 방법을 이용하여 각 대안을 평가하였다. 또한 로버스트한 의사결정을 위해서 통계적인 기법과 maximin, maximax, Hurwicz, equal likelihood criterion 방법을 사용하였다. 그 결과 Fuzzy TOPSIS를 통한 대안의 순위 선정과 로버스트 의사결정기법을 통해 불확실성과 순위의 애매모호함을 완화시켰다. 본 연구에서 제시한 방법은 하수처리수 재이용의 위치선정 뿐만 아니라 다양한 기후변화 시나리오를 고려한 수자원 사업의 우선순위를 결정하는데 사용될 수 있다.

본 논문에서는 수상 및 수중운동체의 안정성 및 안정화기법에 관해 고찰한다. 선박이 운동을 하게 되면 부가질량이 변하게 되고 대칭인 시스템행렬이 비대칭이 된다. 비대칭성에 따라 시스템의 안정성해석방법도 달라지는데 예를 들어 가속도 피드백을 통해 비대칭요소를 제거하여 대칭으로 변환시키는 것이 가장 대표적인 해석 및 안정화 기법이다. 시스템 모델자체는 어디까지나 모델이기 때문에 대상시스템을 명확하게 수식으로 표현할 수 없으므로 피드백에 의한 비대칭요소를 소거시키는 방법은 타당하지 못하다. 따라서 본 논문에서는 대칭행렬이 비대칭행렬로 변하는 제약에 구애받지 않는, 보다 일반성을 갖는 안정성해석법을 제안하였다. 그리고 시스템 안정성 조건을 행렬부등식으로 변환하여 나타내었다. 이것은 안정성 해석 및 안정화를 위한 제어이득을 효율적으로 계산할 수 있는 방법으로 다양한 제약조건에도 유연하게 대응할 수 있다. 시뮬레이션을 통해 제안한 기법의 유효성을 검증하였다.

This paper presents a robust lane detection algorithm based on RGB color and shape information during autonomous car control in realtime. For realtime control, our algorithm increases its processing speed by employing minimal elements. Our algorithm extracts yellow and white pixels by computing the average and standard deviation values calculated from specific regions, and constructs elements based on the extracted pixels. By clustering elements, our algorithm finds the yellow center and white stop lanes on the road. Our algorithm is insensitive to the environment change and its processing speed is realtime-executable. Experimental results demonstrate the feasibility of our algorithm.

It is very important for a mobile robot to recognize and model its environments for navigation. However, the grid map constructed by sonar sensors cannot accurately represent the environment, especially the narrow environment, due to the angular uncertainty of sonar data. Therefore, we propose a map building scheme which combines sonar sensors and IR sensors. The maps built by sonar sensors and IR sensors are combined with different weights which are determined by the degree of translational and rotational motion of a robot. To increase the effectiveness of sensor fusion, we also propose optimal sensor arrangement through various experiments. The experimental results show that the proposed method can represent the environment such as narrow corridor and open door more accurately than conventional sonar sensor-based map building methods.

본 논문은 얼굴의 표정과 몸 동작을 광학식 동작 포착장비를 활용하여 동시에 포착하는 경우에 있어 얼굴 부위 마커들에 대한 노이즈에 강건한 데이터 처리 방법에 대해 다룬다. 일반적인 얼굴 표정만 포착하는 경우와 달리, 몸의 움직임과 동시에 포착할 경우 포착용 카메라가 멀리 있어 얼굴에 붙인 마커들의 궤적 데이터는 특별한 처리를 요한다. 특히 궤적의 표식화, 빈 곳 메우기, 노이즈 제거의 과정이 필수적이며, 이러한 과정을 위해 본 논문에서는 지역좌표에 기반을 둔 궤적 데이터 처리 방법을 제안한다. 지역 좌표는 강체변형에 불변한 특징이 있으며, 얼굴모양의 국지적인 변화를 의미하여, 궤적 데이터처리에 효과적으로 활용 될 수 있음을 보였다. 또한 제안한 방법을 활용하여 애니메이션을 제작해 실제 제작 환경에 적용 가능함을 보였다.

저등급 석탄인 갈탄(lignite)을 순환 유동층 가스화기(circulating fluidized bed gasifier)의 효과적인 가스화를 위한 공급탄으로 제조하기 위하여 모든 조건들은 동일하고 스크린의 크기만을 변경하여 목적하는 입도분포 특성을 달성하는 최적조건을 찾기 위한 실험을 수행하였다. 가스화기 공급탄은 0.045~1 mm 크기로 85 wt% 이상이 요구되며 이러한 입도분포를 갖는 공급탄을 제조하기 위해서는 경제적이면서도 효과적인 공정 설계가 반드시 필요하다. 따라서 본 연구는 중국산 갈탄을 해머밀로 효과적으로 분쇄하기 위하여 다구치 설계를 사용하였으며, 설계조건에 따른 실험결과 및 통계분석 결과 95% 유의수준에서 1차 스크린의 크기는 3 mm, 2차 스크린의 크기는 1.3 mm인 경우가 최적화된 조건인 것으로 나타났다.

Finding a head of a person in a scene is very important for taking a well composed picture by a robot photographer because it depends on the position of the head. So in this paper, we propose a robust head tracking algorithm using a hybrid of an omega shape tracker and local binary pattern (LBP) AdaBoost face detector for the robot photographer to take a fine picture automatically. Face detection algorithms have good performance in terms of finding frontal faces, but it is not the same for rotated faces. In addition, when the face is occluded by a hat or hands, it has a hard time finding the face. In order to solve this problem, the omega shape tracker based on active shape model (ASM) is presented. The omega shape tracker is robust to occlusion and illumination change. However, when the environment is dynamic, such as when people move fast and when there is a complex background, its performance is unsatisfactory. Therefore, a method combining the face detection algorithm and the omega shape tracker by probabilistic method using histograms of oriented gradient (HOG) descriptor is proposed in this paper, in order to robustly find human head. A robot photographer was also implemented to abide by the 'rule of thirds' and to take photos when people smile.

Visual odometry is a popular approach to estimating robot motion using a monocular or stereo camera. This paper proposes a novel visual odometry scheme using a stereo camera for robust estimation of a 6 DOF motion in the dynamic environment. The false results of feature matching and the uncertainty of depth information provided by the camera can generate the outliers which deteriorate the estimation. The outliers are removed by analyzing the magnitude histogram of the motion vector of the corresponding features and the RANSAC algorithm. The features extracted from a dynamic object such as a human also makes the motion estimation inaccurate. To eliminate the effect of a dynamic object, several candidates of dynamic objects are generated by clustering the 3D position of features and each candidate is checked based on the standard deviation of features on whether it is a real dynamic object or not. The accuracy and practicality of the proposed scheme are verified by several experiments and comparisons with both IMU and wheel-based odometry. It is shown that the proposed scheme works well when wheel slip occurs or dynamic objects exist.

Recently, many vision-based navigation methods have been introduced as an intelligent robot application. However, many of these methods mainly focus on finding an image in the database corresponding to a query image. Thus, if the environment changes, for example, objects moving in the environment, a robot is unlikely to find consistent corresponding points with one of the database images. To solve these problems, we propose a novel navigation strategy which uses fast motion estimation and a practical scene recognition scheme preparing the kidnapping problem, which is defined as the problem of re-localizing a mobile robot after it is undergone an unknown motion or visual occlusion. This algorithm is based on motion estimation by a camera to plan the next movement of a robot and an efficient outlier rejection algorithm for scene recognition. Experimental results demonstrate the capability of the vision-based autonomous navigation against dynamic environments.

This paper presents a multi-robot localization based on multidimensional scaling (MDS) in spite of the existence of incomplete and noisy data. While the traditional algorithms for MDS work on the full-rank distance matrix, there might be many missing data in the real world due to occlusions. Moreover, it has no considerations to dealing with the uncertainty due to noisy observations. We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nyström approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

A robust position-sensing system is proposed in this paper for ubiquitous mobile robots which move indoor as well as outdoor. The Differential GPS (DGPS) which has position estimation error of less than 5 m is a general solution when the mobile robots are moving outdoor, while an active beacon system (ABS) with embedded ultrasonic sensors is selected as an indoor positioning system. The switching from the outdoor to indoor or vice versa causes unstable measurements on account of the reference and algorithm changes. To minimize the switching time in the position estimation and to stabilize the measurement, a robust position-sensing system is proposed. In the system, to minimize the switching delay, the door positions are stored and updated in a database. The reliability and accuracy of the robust positioning system based on DGPS and ABS are verified through the real experiments using a mobile robot prepared for this research and demonstrated.

비선형 시스템의 제어를 위해서 일반적으로 사용하는 하나의 동작점에서 얻은 선형모델에 기초한 제어기는 여러 가지 선형제어기법을 응용할 수 있다는 장점이 있지만 파라미터 변동에 대해 적절히 대처하지 못하는 단점을 가진다. 따라서 본 논문에서는 제어대상의 시스템 파라미터가 변동하는 경우에도 제어기가 적응적으로 작동하기 위한 방법으로 보간-LQ 제어기법을 제안한다. 보간-LQ 제어기는 다수개의 서브-LQ 제어기를 설계하고, 이들의 이득을 다시 보간하는 방법을 사용하기 때문에 파라미터 변동에 따라 제어기의 이득이 지속적으로 조정되는 제어기이다. 제안한 방법은 파라미터 변동이 존재하는 컨테이너 크레인 시스템에 적용하여 유효성을 살펴본다.

This paper introduces a position-based robust visual servoing method which is developed for operation of a human-like robot with two arms. The proposed visual servoing method utilizes SIFT algorithm for object detection and CAMSHIFT algorithm for object tracking. While the conventional CAMSHIFT has been used mainly for object tracking in a 2D image plane, we extend its usage for object tracking in 3D space, by combining the results of CAMSHIFT for two image plane of a stereo camera. This approach shows a robust and dependable result. Once the robot's task is defined based on the extracted 3D information, the robot is commanded to carry out the task. We conduct several position-based visual servoing tasks and compare performances under different conditions. The results show that the proposed visual tracking algorithm is simple but very effective for position-based visual servoing.

The problem of establishing the servo system to reach the desired location keeping all features in the field of view and following a straight line is considered. In addition, robustness of camera calibration parameters is considered in this paper. The proposed approach is based on switching from position-based visual servoing (PBVS) to image-based visual servoing (IBVS) and allows the camera path to follow a straight line. To achieve the objective, a pose estimation method is required; the camera's target pose is estimated from the obtained images without the knowledge of the object. A switched control law moves the camera equipped to a robot end-effector near the desired location following a straight line in Cartesian space and then positions it to the desired pose with robustness to camera calibration error. Finally simulation results show the feasibility of the proposed visual servoing technique.