The containment liner plate (CLP) in a nuclear power plant is the most critical part of the structure of a power plant, as it prevents the radioactive contamination of the surrounding area. This paper presents feasibility of structural health monitoring (SHM) and an elastic wave tomography method based on ultrasonic guided waves (GW), for evaluating the integrity of CLP. It aims to check the integrity for a dynamic response to a damaged isotropic structure. The proposed SHM technique relies on sensors and, therefore, it can be placed on the structure permanently and can monitor either passively or actively. For applying this method, a suitable guided wave mode tuning is required to verify wave propagation. A finite element analysis (FEA) is performed to figure out the suitable GW mode for a CLP by considering geometric and material condition. Furthermore, elastic wave tomography technique is modified to evaluate the CLP condition and its visualization. A modified reconstruction algorithm for the probabilistic inspection of damage tomography algorithm is used to quantify corrosion defects in the CLP. The location and shape of the wall-thinning defects are successfully obtained by using elastic GW based SHM. Making full use of verified GW mode to Omni-directional transducer, it can be expected to improve utilization of the SHM based evaluation technique for CLP.

In this study, a modified imaging algorithm was implemented to improve the imaging accuracy for defects located on a structure. Based on analysis of the Lamb wave mode, a guided ultrasonic wave inspection technique was applied, which was able to illustrate images of defects in a 6 mm steel plate simulating containment liner plate (CLP) in nuclear power plants. The dominant Lamb wave mode was determined through short-time Fourier transform waveform analysis and imaging verification. Following tomography verification, limitations of the antisymmetric mode in the thick steel plate were identified. In addition, a modified shape factor, based on the energy distribution factor according to the beam pattern and beam width, was suggested for field applications and improved imaging accuracy. Results of the analysis revealed a beam skewing phenomenon for the Lamb wave mode. In the case of S0 2.7 MHz·mm, skewing as well as distortion effects are not observed in the experiment, while the S0 modes at 2.64 and 2.74 MHz·mm show either of them. Considering skewing width, the size of the shape function was modified. Application of the modified shape function allows us to obtain more accurate image to actual defect shape.

Functional near-infrared spectroscopy (fNIRS) technology has been widely used to analyze biomechanics and diagnose brain activity. Despite being a promising tool for assessing the brain cortex status, this system is susceptible to disturbances and noise from electrical instrumentation and basal metabolism. In this study, an alternative filtering method, maximum likelihood generalized extended stochastic gradient (ML-GESG) estimation, is proposed to overcome the limitations of these disturbance factors. The proposed algorithm was designed to reduce multiple disturbances originating from heartbeats, breathing, shivering, and instrumental noises as multivariate parameters. To evaluate the effectiveness of the algorithm in filtering involuntary signals, a comparative analysis was conducted with a conventional filtering method, using hemodynamic responses to auditory stimuli and psycho-acoustic factors as quality indices. Using auditory sound stimuli consisting of 12 voice sources (six males and six females), the fNIRS test was configured with 18 channels and conducted on 10 volunteers. The psycho-acoustic factors of loudness and sharpness were used to evaluate physiological responses to the stimuli. Applying the proposed filtering method, the oxygenated hemoglobin concentration correlated better with the psychoacoustic analysis of each auditory stimulus than that of the conventional filtering method.

In this paper, a detection algorithm using ultrasonic guided wave phenomena to detect damaged regions of aluminum plates (Al 6061-T6) is studied. For damaged regions with a sort of corrosion defect caused by exposure to moisture or electrolyte solutions, the detectability is evaluated by both attenuating and frequency-level-changing of guided waves. The suggested guided wave algorithm is categorized using linear and nonlinear assessment, which can be for macro-scale and micro-scale (e.g. plasticity, dislocation) defects. To apply nonlinearity of the guided wave, in damage-free condition, accumulated effects are verified with respect to propagation distance. And, the sensitivity of the accumulated effect is also verified under corrosion condition by comparing it with the case of the damage-free condition. The visualization algorithm, illustrating the corrosion area, is modified considering the accumulated effect and attenuation. Damage tomography shows good precision for both corrosion and wall-thinning regions. For the case of multiple corrosion-damaged regions, the suggested tomographic algorithm yields some hazy images, which demand additional fault decision mechanisms. To overcome this limitation, the faster R-CNN algorithm can act as an alternative algorithm to identify the possibility of damage. By comparing the extended transducer set experiment and the faster R-CNN object-detection method, the suggested object-detecting algorithm is verified; it can conclusively distinguish damaged areas.