부유식 해상태양광 설비는 패널 지지를 위한 프레임 구조물, 구조체의 부력 제공을 위한 부유체와 전체 시스템의 거동을 제한하는 계류시설로 구성되어 있다. 계류시설은 구조물의 지지조건으로서의 역 할을 통해 안정적인 발전량 수급에 기여한다. 하지만 해당 시스템은 설치된 해상환경 특성상 계류선의 파단 및 손상 시 직접적인 탐지가 불가능해 유지관리에 어려움이 있다. 따라서 본 연구에서는 패널지 지 프레임 구조체에 가속도 센서 부착을 가정하여, 해당 센서 계측값을 토대로 계류설비에서 발생한 파단 및 손상이 발생한 위치를 추정하는 알고리즘을 개발하였다. 알고리즘은 비지도학습 인공지능의 일종인 오토인코더를 활용하여 가속도 계측값의 재현 과정을 통해 정상상태의 구조 응답을 학습한 모 델이 비정상상태의 계측값을 재현 시 발생한 오차를 통해 손상 발생 여부와 위치를 실시간 탐지하도 록 구성하였다. 정상상태 구조응답을 기반으로 한 모델의 학습을 위해 패널지지 구조체를 10x10 격자 형으로 결합한 다중 결합 시스템에 불규칙파랑을 환경하중으로 적용함을 통해 발생한 6자유도 가속도 데이터를 확보하였다. 계류시설의 손상 발생 시 주된 변화 인자 탐지를 위해 상관성 분석과 민감도 분 석을 실시하여 손상 위치 추정 알고리즘에 적용할 주요 인자를 선별하여 학습 및 추정 성능에 대한 비교 분석을 수행하였다. 구축된 알고리즘의 테스트를 위해 총 5개 종 손상 케이스 데이터셋을 구축하 여 손상 위치 추정 성능을 비교하였다. 본 연구를 통해 계류 시설에 발생한 손상 여부 및 위치를 추정 하여 부유식 해상태양광 설비의 선제적 유지관리에 기여할 수 있을 것으로 기대된다.

In this study, we present an algorithm for indoor robot position estimation. Estimating the position of an indoor robot using a fixed imaging device obviates the need for complex sensors or hardware, enabling easy estimation of absolute position through marker recognition. However, location estimation becomes impossible when the device moves away from the surrounding obstacles or the screen, presenting a significant drawback. To solve this problem, we propose an algorithm that improves the precision of robot indoor location estimation using a Gaussian Mixture Model(GMM) and a Kalman filter estimation model. We conducted an actual robot operation experiment and confirmed accurate position estimation, even when the robot was out of the image.

This study aims to investigate and reveal the spatial structure of Boryeonghyeon by examining the geographical status of its Eupchi (Local administrative center:邑治) through an analysis of the location, tracing locations of governemnt offices including Dongheon(東軒) and Kaeksa(客舍) in the walled town, and checking the lot numbers of Sajikdan(社稷壇), Yeodan(厲壇), and Cheongyeonyeok(靑淵驛) outside it. Buildings of Boryeonghyeon in the walled town in the Joseon Dynasty were almost lost and now, part of the city wall and Haesanru(海山樓) just remains as relic. The walled town consisted of several buildings of government offices as well as Dongheon and Kaeksa which are government organs. Altar and shrine(壇廟) facilities including Shrine of Confucius(文廟), Altar of Land and Grain, and Preceptor's Shrine were placed outside the walled town and Cheongyeonyeok were operated as the facilities for transmission of royal orders. Therefore, the government office facilities in the walled town, altar and shrine facilities outside the fortress, and the location of the post station were required to trace and check each of them. For the checking method, the lot numbers could be checked by checking the original cadastral maps and the then land categories and owners, analyzing the records and circumstances of the relevant township annals(邑誌), and examining analyses on the locations by using a numerical map of one to 5 thousands. The study estimated the locations of government facilities including Dongheon and Kaesa placed in the walled town and was grasped to be the east and west gates with the south gate which remains now in the fortress. And the lot numbers of Sajikdan, Yeodan, Cheongyeonyeok.

안전한 구조부재의 위치파악, 정밀한 시공을 위하여 인양, 안착시스템의 위치추정은 매우 중요한 요소이다. 크레인 인양시스템, 침매터널 이동시스템은 운용 중에 돌풍, 파랑과 같은 외란에 노출되어 불필요한 진동이 유발되고, 인양 길이 등이 시간에 따라 변하는 시변 시스템으로 구조부재나 질량체의 설계위치이동에 방해 요인이 되고 있다. 본 연구에서는 베이지안 필터가 가지는 비가우시안 잡음, 시스템 비선형성에 대한 확장성을 이용하여 정교하게 위치를 추정하는 기법을 다룬다. 이를 위하여 베이지안 프러세스로부터 필터의 유도과정을 살펴보고 유도된 확장 칼만필터를 이용하여 시변 진자시스템의 위치 추정 성능을 평가하여 보았다. 시변 진자시스 템에 대한 수치 시뮬레이션결과 외란에 의한 진동, 시스템의 비선형성, 시변성에 대해 안정적으로 위치를 추정하는 것을 알 수 있었다.

The Location Based Service is growing rapidly nowadays due to the universalization of the use for smartphone, therefore the location determination technology has been placed in an important position. This study suggests a method that can provide the estimate of users’ location by using PDR method and smartphone geomagnetic sensor data. This method assists the measure of enhancing the accuracy of indoor localization. Moreover, it is to study ways to provide the exact indoor layout for evacuating the workers in emergency such as fires and natural disasters.

The LBS(Location Based Service) technology plays an important role in reducing wastes of time, losses of human lives and economic losses by detecting the user's location in order by suggesting the optimal evacuation route of the users in case of safety accidents. We developed an algorithm to estimate indoor location, movement path and indoor location changes of smart phone users based on the built-in sensors of smartphones and the dead-reckoning algorithm for pedestrians without a connection with smart devices such as Wi-Fi and Bluetooth. Furthermore, seven different indoor movement scenarios were selected to measure the performance of this algorithm and the accuracy of the indoor location estimation was measured by comparing the actual movement route and the algorithm results of the experimenter(pedestrian) who performed the indoor movement. The experimental result showed that this algorithm had an average accuracy of 95.0%.

The Location Based Service is growing rapidly nowadays due to the universalization of the use for smartphone, and therefore the location determination technology has been placed in a very important position. this study suggests an algorithm that can provide the estimate of users' location by using smartphone sensors. And in doing so we will propose a methodology for the creation and update of indoor map though the more accurate position estimation using smartphone sensors such as acceleration sensor, gyroscope sensor, geomagnetic sensor and rotation sensor.

The key motivation of this study is for a style of the sensor arrangement to have an effect on the localization performance of mobile robots in case of using sonar sensors. In general robot platforms with sonar sensors, sonar sensors are supposed to be radially arranged on their rotational axis of mobile robots. However, relevant limits to several functions required for their autonomous navigation occur unexpectedly, because a sonar sensor generally has the negative nature of its wide beam width together with the specular reflection. We present a new strategy of the sonar sensor arrangement capable of enhancing the localization performance. Sonar sensors are intended to be arranged nonradially (twistedly expressed in this paper) on their rotational axis. The localization scheme called STARER: Sonar-Twisted ARrangement localizER is based on the extended Kalman filter (EKF) with occupancy grid maps. Experimental results demonstrate the validity and robustness of the proposed method for the localization of mobile robots.

The angle of attack of a cambered otter board in a bottom trawl was estimated using a three-dimensional semi-analytic treatment of a towing cable (warp) system that was applied to the field experiments of a bottom trawl obtained by the Scanmar system. The equilibrium condition of the horizontal component and vertical component of forces was used to the three forces acting on the otter board in the horizontal plane. Those forces were the force on the warp at the bracket, hydrodynamic lift and drag forces on the otter board and the force on the hand rope attached just behind the otter board. Also the equilibrium of moment about the front edge of the otter board was used to find out the angle of attack of the cambered otter board. When the warp length was 120m and 180m long and the towing speed was between 1.23 and 1.90 m/s, the estimated angle of attack of the cambered otter board was ranged between 26.1° and 29.6°, respectively, though the maximum lift force was at the angle of attack 22.6°. The angle of attack of the otter board was tended to increase weakly with the longer length of warp (180 m) at the same towing speed in the experiment.

This paper shows how effectively sonar data can be worked with approaches suggested for the indoor SLAM (Simultaneous Localization And Mapping). A sonar sensor occasionally provides wrong distance range due to the wide beam width and the specular reflection phenomenon. To overcome weak points enough to use for the SLAM, several approaches are proposed. First, distance ranges acquired from the same object have been stored by using the FPA (Footprint-association) model, which associates two sonar footprints into a hypothesized circle frame. Using the Least Squares method, a line feature is extracted from the data stored through the FPA model. By using raw sonar data together with the extracted features as observations, the visibility for landmarks can be improved, and the SLAM performance can be stabilized. Additionally, the SP (Symmetries and Perturbations) model, a representation of uncertain geometric information that combines the probability theory and the theory of symmetries, is applied in this paper. The proposed methods have been tested in a real home environment with a mobile robot.

This study aims to infer the plan and location of the government office building in Chongju Castle in the Late Yi-dynasty. The conclusion is as follows: 1. The Chongju Castle Map(淸州邑城圖, late in the 18th century, hereinafter referred to CCM) provides the detail arrangement and location of Government Office Building in Chongju Castle. And the road structure and plan of the CCM is almost same with the present time. 2. As compared with CCM and a Chongju-land Registration Map(淸州面地籍原圖, 1913, CRM) to infer the location of the traditional government office building in Chongju Castle, the building locations of Gaek-Sa(客舍) Donghun(東軒)'s region in CCM are almost accordance with today's. But those of Byungyoung(兵營) Group's region are represented by a little error. So the locations of Byungyoung(兵營) Group's region rearranged, moved down to be in accordance with the approach circulation of Main Gate(閉門樓) which is shown in CRM. 3. The records, on the plan of the traditional government office building in Chongju Castle, have proved that the plan of Gaek-Sa was a width of 11 bay and a depth of 2 bay. A width of 3 bay drawn in CCM, the present plan of Donghun is a width of 7 bay and a depth of 4 bay. The main building and especially the double-storied Main Gate($4{\times}3$ 수식 이미지) of Byungyoung Group are exactly in keeping with the present road structure.

Multi-floor navigation of a mobile robot requires a technology that allows the robot to safely get on and off the elevator. Therefore, in this study, we propose a method of recognizing the elevator from the current position of the robot and estimating the location of the elevator locally so that the robot can safely get on the elevator regardless of the accumulated position error during autonomous navigation. The proposed method uses a deep learning-based image classifier to identify the elevator from the image information obtained from the RGB-D sensor and extract the boundary points between the elevator and the surrounding wall from the point cloud. This enables the robot to estimate the reliable position in real time and boarding direction for general elevators. Various experiments exhibit the effectiveness and accuracy of the proposed method.

In this paper, we propose an algorithm that estimates the location of lunar rover using IMU and vision system instead of the dead-reckoning method using IMU and encoder, which is difficult to estimate the exact distance due to the accumulated error and slip. First, in the lunar environment, magnetic fields are not uniform, unlike the Earth, so only acceleration and gyro sensor data were used for the localization. These data were applied to extended kalman filter to estimate Roll, Pitch, Yaw Euler angles of the exploration rover. Also, the lunar module has special color which can not be seen in the lunar environment. Therefore, the lunar module were correctly recognized by applying the HSV color filter to the stereo image taken by lunar rover. Then, the distance between the exploration rover and the lunar module was estimated through SIFT feature point matching algorithm and geometry. Finally, the estimated Euler angles and distances were used to estimate the current position of the rover from the lunar module. The performance of the proposed algorithm was been compared to the conventional algorithm to show the superiority of the proposed algorithm.

Planetary global localization is necessary for long-range rover missions in which communication with command center operator is throttled due to the long distance. There has been number of researches that address this problem by exploiting and matching rover surroundings with global digital elevation maps (DEM). Using conventional methods for matching, however, is challenging due to artifacts in both DEM rendered images, and/or rover 2D images caused by DEM low resolution, rover image illumination variations and small terrain features. In this work, we use train CNN discriminator to match rover 2D image with DEM rendered images using conditional Generative Adversarial Network architecture (cGAN). We then use this discriminator to search an uncertainty bound given by visual odometry (VO) error bound to estimate rover optimal location and orientation. We demonstrate our network capability to learn to translate rover image into DEM simulated image and match them using Devon Island dataset. The experimental results show that our proposed approach achieves ~74% mean average precision.

Acoustic based localization is essential to operate autonomous robotic systems in underwater environment where the use of sensorial data is limited. This paper proposes a localization method using artificial underwater acoustic sources. The proposed method acquires directional angles of acoustic sources using time difference of arrivals of two hydrophones. For this purpose, a probabilistic approach is used for accurate estimation of the time delay. Then, Gaussian sum filter based SLAM technique is used to localize both acoustic sources and underwater vehicle. It is performed by using bearing of acoustic sources as measurement and inertial sensors as prediction model. The proposed method can handle directional ambiguity of time difference based source localization by generating Gaussian models corresponding to possible locations of both front and back sides. Through these processes, the proposed method can provide reliable localization method for underwater vehicles without any prior information of source locations. The performance of the proposed method is verified by experimental results conducted in a real sea environment.