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Structural health monitoring (SHM) is a technique to diagnose an accurate and reliable condition of civil infrastructure by collecting and analyzing responses from distributed sensors. In recent years, aging civil structures have been increasing and they require further developed SHM technology for development of sustainable society. Wireless smart sensor and network technology, which is one of the recently emerging SHM techniques, enables more effective and economic SHM system in comparison to the existing wired systems. Researchers continue on development of the capability and extension of wireless smart sensors, and implement performance validation in various in-laboratory and outdoor full-scale experiments. This paper presents a summary of recent (mostly after 2010) researches on smart sensors, focused on the newly developed hardware, software, and validation examples of the developed smart sensors.ope

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????????? ????????? ????????? ?????? ????????? ????????? ??????????????? ????????? ??? ?????? ???????????? ?????? ??????????????? ?????? ????????? ?????? ??????. ????????? ????????? ??????????????? ?????? ????????? ???????????? ???????????? ?????? ????????? ?????? ?????? ????????? ???????????? ?????????, ?????? ??? ??????(O&M, Operation and Management) ??????????????? ????????? ????????? ?????? ??? ??????????????? ????????? ?????? ??????????????? ???????????? ??????. ????????? ????????? ?????? ?????? ?????? ??? ?????? ?????? ???, ?????? ??????????????? ????????? ???????????? ????????? ?????? ???????????? ???????????? ?????????, ???????????? ??????????????? ?????? ????????? ??? ?????? ????????? ??? ??? ??????. ????????? ?????? ??????????????? ?????? ???????????? ?????? ????????? ????????? ????????? ??????????????? ?????????????????? ?????? ???????????? ?????????. ????????? ??? ??????????????? (1) ????????? ???????????? ?????? ???????????? ????????? ???????????? ??????????????? ????????? ???????????? ????????? ????????????, (2) ????????? ????????? ????????? ???????????? ?????? NREL 5 MW??? ?????? ????????? ???????????? ???????????? ???, ???????????? ?????? ?????? ??? ?????? ???????????? ?????? ?????? ????????? ???????????? ??????????????????, ?????? ?????? ????????? ?????? ???????????? ????????? ???????????????.ope

Multisensor Fusion of Landsat Images for High-Resolution Thermal Infrared Images Using Sparse Representations

Land surface temperature (LST) is an important parameter in the analysis of climate and human-environment interactions. Landsat Earth observation satellite data including a thermal band have been used for environmental research and applications; however, the spatial resolution of this thermal band is relatively low. This study investigates an efficient method of fusing Landsat panchromatic and thermal infrared images using a sparse representation (SR) technique. The application of SR is used for the estimation of missing details of the available thermal infrared (TIR) image to enhance its spatial features. First, we propose a method of building a proper dictionary considering the spatial resolution of the original thermal image. Second, a sparse representation relation between low- and high-resolution images is constructed in terms of the Landsat spectral response. We then compare the fused images created with different sampling factors and patch sizes. The results of both qualitative and quantitative evaluation show that the proposed method improves spatial resolution and preserves the thermal properties of basic LST data for use with environmental problems

AD-YOLO: You Look ONly Once in Training Multiple Sound Event Localization and Detection

Sound event localization and detection (SELD) combines the identification of sound events with the corresponding directions of arrival (DOA). Recently, event-oriented track output formats have been adopted to solve this problem; however, they still have limited generalization toward real-world problems in an unknown polyphony environment. To address the issue, we proposed an angular-distance-based multiple SELD (AD-YOLO), which is an adaptation of the "You Look Only Once" algorithm for SELD. The AD-YOLO format allows the model to learn sound occurrences location-sensitively by assigning class responsibility to DOA predictions. Hence, the format enables the model to handle the polyphony problem, regardless of the number of sound overlaps. We evaluated AD-YOLO on DCASE 2020-2022 challenge Task 3 datasets using four SELD objective metrics. The experimental results show that AD-YOLO achieved outstanding performance overall and also accomplished robustness in class-homogeneous polyphony environments.Comment: 5 pages, 3 figures, accepted for publication in IEEE ICASSP 202