Wireless smart sensors enable new approaches to improve structural health monitoring (SHM) practices through the use of distributed data processing. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While much of the technology associated with smart sensors has been available for nearly a decade, there have been limited numbers of fullscale implementations due to the lack of critical hardware and software elements. This research develops a flexible wireless smart sensor framework for full-scale, autonomous SHM that integrates the necessary software and hardware while addressing key implementation requirements. The Imote2 smart sensor platform is employed, providing the computation and communication resources that support demanding sensor network applications such as SHM of civil infrastructure. A multi-metric Imote2 sensor board with onboard signal processing specifically designed for SHM applications has been designed and validated. The framework software is based on a service-oriented architecture that is modular, reusable and extensible, thus allowing engineers to more readily realize the potential of smart sensor technology. Flexible network management software combines a sleep/wake cycle for enhanced power efficiency with threshold detection for triggering network wide operations such as synchronized sensing or decentralized modal analysis. The framework developed in this research has been validated on a full-scale a cable-stayed bridge in South Korea.

Industrialized nations have a huge investment in the pervasive civil infrastructure on which our lives rely. To properly manage this infrastructure, its condition or serviceability should be reliably assessed. For condition or serviceability assessment, Structural Health Monitoring (SHM) has been considered to provide information on the current state of structures by measuring structural vibration responses and other physical phenomena and conditions. Civil infrastructure is typically large-scale, exhibiting a wide variety of complex behavior; estimation of a structure's state is a challenging task. While SHM has been and still is intensively researched, further efforts are required to provide efficient and effective management of civil infrastructure. Efforts toward realization of SHM systems using smart sensors, however, have not resulted in full-fledged applications. All efforts to date have encountered difficulties originating from limited resources on smart sensors (e.g., small memory size, small communication throughput, limited speed of the CPU, etc.). To realize an SHM system employing smart sensors, this system needs to be designed considering both the characteristics of the smart sensor and the structures to be monitored.This research addresses issues in smart sensor usages in SHM applications and realizes, for the first time, a scalable and extensible SHM system using smart sensors. The iv architecture of the proposed SHM is first presented. The Intel Imote2 equipped with an accelerometer sensor board is chosen as the smart sensor platform to demonstrate the efficacy of this architecture. Middleware services such as model-based data aggregation, reliable communication, and synchronized sensing are developed. SHM Algorithms identified as promising for the usage on smart sensor systems are extended to improve practicability and implemented on Imote2s. Careful attention has been paid to integrating these software components so that the system possesses identified desirable features.The damage detection capability and autonomous operation of the developed system are then experimentally verified. The SHM system consisting of ten Imote2s are installed on a scale-model truss. The SHM system monitors the truss in a distributed manner to localize simulated damage.In summary, this thesis proposes and realizes a scalable and autonomous SHM system using smart sensors. The system is experimentally verified to be effective for damage

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We report the results of VERA multi-epoch VLBI 22 GHz water maser observations of S255IR-SMA1, a massive young stellar object located in the S255 star forming region. By annual parallax the source distance was measured as D = 1.78 +0.12 −0.11 kpc and the source systemic motion was (µ α cos δ, µ δ ) = (−0.13 ± 0.20, −0.06 ± 0.27) mas yr −1 . Masers appear to trace a U-shaped bow shock whose morphology and proper motions are well reproduced by a jet-driven outflow model with a jet radius of about 6 AU. The maser data, in the context of other works in the literature, reveal ejections from S255IR-SMA1 to be episodic, operating on timescales of ∼ 1000 years.

We present analyses to determine the fundamental parameters of the Galaxy based on VLBI astrometry of 52 Galactic maser sources obtained with VERA, VLBA and EVN. We model the Galaxy's structure with a set of parameters including the Galaxy center distance R 0 , the angular rotation velocity at the LSR Ω 0 , mean peculiar motion of the sources with respect to Galactic rotation (U src , V src , W src ), rotation-curve shape index, and the V component of the Solar peculiar motions V ⊙ . Based on a Markov chain Monte Carlo method, we find that the Galaxy center distance is constrained at a 5% level to be R 0 = 8.05 ± 0.45 kpc, where the error bar includes both statistical and systematic errors. We also find that the two components of the source peculiar motion U src and W src are fairly small compared to the Galactic rotation velocity, being U src = 1.0 ± 1.5 km s −1 and W src = −1.4 ± 1.2 km s −1 . Also, the rotation curve shape is found to be basically flat between Galacto-centric radii of 4 and 13 kpc. On the other hand, we find a linear relation between V src and V ⊙ as V src = V ⊙ − 19 (±2) km s −1 , suggesting that the value of V src is fully dependent on the adopted value of V ⊙ . Regarding the rotation speed in the vicinity of the Sun, we also find a strong correlation between Ω 0 and V ⊙ . We find that the angular velocity of the Sun, Ω ⊙ , which is defined as Ω ⊙ ≡ Ω 0 + V ⊙ /R 0 , can be well constrained with the best estimate of Ω ⊙ = 31.09 ± 0.78 km s −1 kpc −1 . This corresponds to Θ 0 = 238 ± 14 km s −1 if one adopts the above value of R 0 and recent determination of V ⊙ ∼12 km s −1 .