In the ground environment, mobile robot research uses sensors such as GPS and optical cameras to localize surrounding landmarks and to estimate the position of the robot. However, an underwater environment restricts the use of sensors such as optical cameras and GPS. Also, unlike the ground environment, it is difficult to make a continuous observation of landmarks for location estimation. So, in underwater research, artificial markers are installed to generate a strong and lasting landmark. When artificial markers are acquired with an underwater sonar sensor, different types of noise are caused in the underwater sonar image. This noise is one of the factors that reduces object detection performance. This paper aims to improve object detection performance through distortion and rotation augmentation of training data. Object detection is detected using a Faster R-CNN.

Geomagnetic field signals have potential for use in underwater navigation and geophysical surveys. To map underwater geomagnetic fields, we propose a method that exploits an autonomous surface vehicle. In our system, a magnetometer is rigidly attached to the vehicle and not towed by a cable, minimizing the system’s size and complexity but requiring a dedicated calibration procedure due to magnetic distortion caused by the vehicle. Conventional 2D methods can be employed for the calibration by assuming the horizontal movement of the magnetometer, whereas the proposed 3D approach can correct for horizontal misalignment of the sensor. Our method does not require a supporting crane system to rotate the vehicle, and calibrates and maps simultaneously by exploiting data obtained from field operation. The proposed method has been verified experimentally in inland waters, generating a magnetic field map of the test area that is of much higher resolution than the public magnetic field data.

This study focuses on autonomous exploration based on map expansion for an underwater robot equipped with acoustic sonars. Map expansion is applicable to large-area mapping, but it may affect localization accuracy. Thus, as the key contribution of this paper, we propose a method for underwater autonomous exploration wherein the robot determines the trade-off between map expansion ratio and position accuracy, selects which of the two has higher priority, and then moves to a mission step. An occupancy grid map is synthesized by utilizing the measurements of an acoustic range sonar that determines the probability of occupancy. This information is then used to determine a path to the frontier, which becomes the new search point. During area searching and map building, the robot revisits artificial landmarks to improve its position accuracy as based on imaging sonar-based recognition and EKF-SLAM if the position accuracy is above the predetermined threshold. Additionally, real-time experiments were conducted by using an underwater robot, yShark, to validate the proposed method, and the analysis of the results is discussed herein.

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.

Localization of underwater vehicle is essential to use underwater robotic systems for various applications effectively. For this purpose, this paper presents a method of two-dimensional SLAM for underwater vehicles equipped with two hydrophones. The proposed method uses directional angles for underwater acoustic sources. A target signal transmitted from acoustic source is extracted using band-pass filters. Then, directional angles are estimated based on Bayesian process with generalized cross-correlation. The acquired angles are used as measurements for EKF-SLAM to estimate both vehicle location and locations of acoustic sources. Through these processes, the proposed method provides reliable estimation for two dimensional locations of underwater vehicles. Experimental results demonstrate the performance of the proposed method in a real sea environment.

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.

Acoustic signal is crucial for the autonomous navigation of underwater vehicles. For this purpose, this paper presents a method of acoustic source localization. The proposed method is based on the probabilistic estimation of time delay of acoustic signals received by two hydrophones. Using Bayesian update process, the proposed method can provide reliable estimation of direction angle of the acoustic source. The acquired direction information is used to estimate the location of the acoustic source. By accumulating direction information from various vehicle locations, the acoustic source localization is achieved using extended Kalman filter. The proposed method can provide a reliable estimation of the direction and location of the acoustic source, even under for a noisy acoustic signal. Experimental results demonstrate the performance of the proposed acoustic source localization method in a real sea environment.

In this study, an analysis model with circular arc on the plane is chosen as a representative section of the steel box girder. The steel box girder has thin-walled cross section. When working load is increased in the outer sector, the behavior of the negative reaction and overturning moment are shown up. Curved bridge has a central angle from about 4 to 26 degree. Negative reaction and overturning moment are analyzed according to increase of a central angle from 4 to 26 degree as well as shape factor.

In this paper, the structural characteristics of the tracking-type floating PV generation system are presented. Moreover, the structural safety of each tracking-type floating PV generation system is estimated from the analytical results obtained by the finite element analyses.

The floating PV generation system is consisted of unit structures linked by the hinge type connection because the effect of bending moment in the structural system loaded due to the unstable movement of water surface can be minimized. In this paper, the investigation and development process of floating PV generation unit structure is presented.

Prestressed concrete (PSC) members are readly available in civil engineering applications due to the convenience of construction and easy of quality control in the manufacturing process of the member. Especially, half-depth precast concrete composite slab, which is one of the PSC flexural members is developed recently using the long-line method. The half-depth precast concrete composite slabs are composed of the precast concrete and the in-situ concrete placed at the site. In this paper, we present the results of experimental investigations pertaining to the pretensioning efficiency and the flexural behavior of half-depth precast concrete composite slab which is made of precast PSC manufactured by the long-line method. In the long-line method, the pretensioned precast member is manufactured simultaneously, by tensioning tendons at once. In addition, we suggest the equation that can estimate the flexural strength of half-depth precast concrete composite slab reasonably by considering the effects of rebar embedded in the precast PSC flexural member.

In this paper, we suggest the new floating type photovoltaic energy generation system, which is improved the structural and economical efficiency, compared with the system developed in the previous research. The structural system in new floating type photovoltaic energy generation system reveals better in structural performance.

This paper presents a method of sonar grid map matching for topological place recognition. The proposed method provides an effective rotation invariant grid map matching method. A template grid map is firstly extracted for reliable grid map matching by filtering noisy data in local grid map. Using the template grid map, the rotation invariant grid map matching is performed by Ring Projection Transformation. The rotation invariant grid map matching selects candidate locations which are regarded as representative point for each node. Then, the topological place recognition is achieved by calculating matching probability based on the candidate location. The matching probability is acquired by using both rotation invariant grid map matching and the matching of distance and angle vectors. The proposed method can provide a successful matching even under rotation changes between grid maps. Moreover, the matching probability gives a reliable result for topological place recognition. The performance of the proposed method is verified by experimental results in a real home environment

This paper presents a method of topological modeling using only low-cost sonar sensors. The proposed method constructs a topological model by extracting sub-regions from the local grid map. The extracted sub-regions are considered as nodes in the topological model, and the corresponding edges are generated according to the connectivity between two sub-regions. A grid confidence for each occupied grid is evaluated to obtain reliable regions in the local grid map by filtering out noisy data. Moreover, a convexity measure is used to extract sub-regions automatically. Through these processes, the topological model is constructed without predefining the number of sub-regions in advance and the proposed method guarantees the convexity of extracted sub-regions. Unlike previous topological modeling methods which are appropriate to the corridor-like environment, the proposed method can give a reliable topological modeling in a home environment even under the noisy sonar data. The performance of the proposed method is verified by experimental results in a real home environment.

This study was to analyze changes of landuse and environmental value of cultivate land for eight years from 1999 to 2007 year in greenbelt area, Seoul. Greenbelt area decreased from 166.82 ㎢ in 1999 to 156.50 ㎢ in 2007 according to removal policy. Regarding landuse status in 2007, forest field area accounted for 64.16 %, dry paddy area 4.10 %, facilitated farming area 3.82 %, rice paddy area 2.95 % out of total greenbelt area. Cultivate land occupied wide spaces with dry paddy area, facilitated farming area, tree nursery in Seocho-gu, Gangnam-gu and Gangdong-gu. Changes of landuse were serious in Seocho-gu. The trend of changes of landuse for eight years is that rice paddy area was changed dry paddy area by laying the ground and dry paddy area was changed facilitated farming area for intensive agriculture. Rice paddy area could change without permission by laying the ground in below 50 cm height and it was changed to green houses due to increase in profit and modern policy of agriculture. It is nessary to monitor landuse regularly, improve regulation for change of landuse, compensate a property loss for maintaining environmental value in greenbelt area.

This study was to examine the characteristics of wirdbirds habitat for improvement plan in green corridor. The target site, Gangseo-gu artificial green corridor was set up with the structure in which small scale of core green space with Goongsan and Yeomchang neighborhood parks in urbanized city was connected with the artificial green space with Gongamnaru, Hwanggeumnae neighborhood parks with 28∼42.5 m in width. Wild birds six∼eleven species; Dendrocopos spp, Paradoxornis webbiana, Parus major, Phasianus colchicus, etc. were observed in core green, but wild birds of two∼five species: Columba livuia, Passer montanus, Pica pica, Hypsipetes amaurotis, etc. were observed in artificial green space. Thus wild birds of artificial and generalist species only moved in artificial green space. The artificial green space where vegetation structure was consisted of single-layer with poorness chose target species laying stress on generalist species and edge species of Parus major, P. palustris, Paradoxornis webbiana etc. for short-term and interior species of Dendrocopos major, Picus canus, etc. for long-term. The result suggested enhancement methods for target species's habitat in green corridor: to secure at least a corridor 30 meters in artificial corridor, to secure ecological pond, to offer the various shelterer and environment of prey-resources through the multi-layer structure.

We present an implementation of particle filter algorithm for global localization and kidnap recovery of mobile robot. Firstly, we propose an algorithm for efficient particle initialization using sonar line features. And then, the average likelihood and entropy of normalized weights are used as a quality measure of pose estimation. Finally, we propose an active kidnap recovery by adding new particle set. New and independent particle set can be initialized by monitoring two quality measures. Added particle set can re-estimate the pose of kidnapped robot. Experimental results demonstrate the capability of our global localization and kidnap recovery algorithm.

Improving practicality of SLAM requires various sensors to be fused effectively in order to cope with uncertainty induced from both environment and sensors. In this case, combining sonar and vision sensors possesses numerous advantages of economical efficiency and complementary cooperation. Especially, it can remedy false data association and divergence problem of sonar sensors, and overcome low frequency SLAM update caused by computational burden and weakness in illumination changes of vision sensors. In this paper, we propose a SLAM method to join sonar sensors and stereo camera together. It consists of two schemes, extracting robust point and line features from sonar data and recognizing planar visual objects using multi-scale Harris corner detector and its SIFT descriptor from pre-constructed object database. And fusing sonar features and visual objects through EKF-SLAM can give correct data association via object recognition and high frequency update via sonar features. As a result, it can increase robustness and accuracy of SLAM in indoor environment. The performance of the proposed algorithm was verified by experiments in home –like environment.

In order to evaluate the relationship between geochemical characteristics and benthic facies of the sediments from the Oenaro Island where red tide proliferation is first observed every year including this year, surface and short multiple core sediment samples were analyzed in terms of geochemical and benthic facies variation. The contents of organic carbon, carbonate, and sulfide gas were relatively low. The variation in C/N ratios, which indicate nature of organic carbon, suggested that the organic carbon recorded in the study area is composed of mixtures of marine and terrigenous organic matters. The concentration of minor elements found at the surface and multiple core sediment samples were also low as well as the enrichment factors(Ef) for the seven heavy metals indicated that the sediment of this area is not polluted significantly. The macrobenthic faunal community comprised 61 species, and their mean density was 708 ind./m2. Polychaete worms were major taxa of this benthic community. A crustacean amphipod, Melita sp. was the most abundant species accounted for 20.7% of total abundance, and the small polychaetes such as Heteromastus filiformis, Paralacydonia poradoxa, Magelona japonica, and Sigambra tentaculata were the next dominant species. The macrobenthos around the Oenaro Island were more diverse and abundant than that in Gamak Bay. The benthic communities in the study area sustained somewhat different species composition based on the cluster analysis and the MDS ordination. The benthic community health condition at three stations seemed to be unbalanced, and slightly polluted based on the biological index such as BPI and BC. There was no clear relationship between the geochemistry characteristic and the benthic faunal facies attributed by the micro-algal blooms in this coastal area.