Long-Distance and Low-Radiation Waveguide Antennas for Wireless Communication Systems inside Tunnels

Wireless LAN usage is also increasing at construction and civil engineering sites, and the efficiency of ICT construction has increased due to the use of tablet PCs and network cameras. When constructing a wireless LAN environment, for example, a LAN cable may be laid from outside the tunnel, and a number of wireless access points (APs) may be installed. However, it is not advantageous to use a large number of APs because the system price increases significantly. We consider using a long leaky-wave antenna to provide one AP. The reason for using a leaky-wave antenna is that, since the total tunnel length is on the order of km, it is necessary to reduce the power radiated by the antenna as much as possible to provide a functional communication area over a long distance. To reduce such transmission losses, we used a waveguide. A waveguide is a low-loss line and can function as a low-loss and low-radiation leaky-wave antenna which is suitable for long-distance communications; this is accomplished by combining a waveguide with a low-radiation antenna mechanism. In this chapter, we report the development of a waveguide-type leaky-wave antenna and the development of a wireless LAN environment in a tunnel

Consistent map building in petrochemical complexes for firefighter robots using SLAM based on GPS and LIDAR

The objective of this study was to achieve simultaneous localization and mapping (SLAM) of firefighter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle filters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The difference between the original Fast-SLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was significant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refinery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability

太平洋における粒子付着性及び自由生活性細菌の多様性・群集構造・機能に関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学准教授 濵﨑 恒二, 東京大学教授 木暮 一啓, 東京大学教授 津田 敦, 東京大学教授 永田 俊, 長崎大学教授 和田 実University of Tokyo(東京大学

Isotopic analysis of Ni, Cu, and Zn in freshwater for source identification

Nickel (Ni), copper (Cu), and zinc (Zn) are commonly used in human activities and pollute aquatic environments including rivers and oceans. Recently, Ni, Cu, and Zn isotope ratios have been measured to identify their sources and cycles in environments. We precisely determined the Ni, Cu, and Zn isotope ratios in rain, snow, and rime collected from Uji City and Mt. Kajigamori in Japan, and investigated the potential of isotopic ratios as tracers of anthropogenic materials. The isotope and elemental ratios suggested that road dust is the main source of Cu in most rain, snow, and rime samples and that some of the Cu may originate from fossil fuel combustion. Zinc in the rain, snow, and rime samples may be partially attributed to Zn in road dust. Zinc isotope ratios in the Uji rain samples are lower than those in the road dust, which would be emitted via high temperature processes. Nickel isotope ratios are correlated with V/Ni ratios in the rain, snow, and rime samples, suggesting that their main source is heavy oil combustion. Furthermore, we analyzed water samples from the Uji and Tawara Rivers and the Kakita River spring in Japan. Nickel and Cu isotope ratios in the river water samples were significantly heavier than those in rain, snow, and rime samples, while Zn isotope ratios were similar. This is attributed to isotopic fractionation of Cu and Ni between particulate-dissolved phases in river water or soil

Does respiratory drive modify the cerebral vascular response to changes in end‐tidal carbon dioxide?

What is the central question of this study? An interaction exists between the regulatory systems of respiration and cerebral blood flow (CBF), because of the same mediator (carbon dioxide, CO ) for both physiological systems. The present study examined whether the traditional method for determining cerebrovascular reactivity to CO (cerebrovascular reactivity; CVR) is modified by changes in respiration. What is the main finding and its importance? CVR was modified by voluntary changes in respiration during hypercapnia. This finding suggests that an alteration in the respiratory system may under- or over-estimate CVR determined by traditional methods in healthy adults.The cerebral vasculature is sensitive to changes in the arterial partial pressure of carbon dioxide (CO ). This physiological mechanism has been well established as a cerebrovascular reactivity to CO (CVR). However, arterial CO may not be an independent variable in the traditional method to assess CVR since the cerebral blood flow (CBF) response is partly affected by the activation of respiratory drive or higher centers in the brain. We hypothesized that CVR is modified by changes in respiration. To test our hypothesis, in the present study, ten young healthy subjects performed hyper- or hypo-ventilation to change end-tidal CO (P CO ) under different concentrations of CO gas inhalation (0, 2.0, 3.5%). We measured middle cerebral artery mean blood flow velocity (MCAVm) by transcranial Doppler to identify the CBF response to change in P CO during each condition. At each F CO condition, P CO was significantly altered by changes in ventilation, and MCA Vm changed accordingly. However, the relationship between changes in MCV Vm and P CO as a response curve of CVR was reset upwards and downwards by hypo- and hyper-ventilation, respectively, compared with CVR during normal-ventilation. The findings of the present study may provide the possibility that an alteration in respiration under- or over-estimates CVR determined by the traditional methods

Consistent map building in petrochemical complexes for frefghter robots using SLAM based on GPS and LIDAR

The objective of this study was to achieve simultaneous localization and mapping (SLAM) of frefghter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle flters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The diference between the original FastSLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was signifcant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refnery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability