NIOM Analysis of Earthquake Observation Records in a Rockfill Dam

ABSTRACT We applied the normalized input–output minimization method (a method developed for the analysis of propagation times in vertical array records) to long-term earthquake observation records from Aratozawa Dam (in Kurihara, Miyagi prefecture, Japan), spanning the period from July 1992 to December 2019 to determine the propagation velocity of seismic waves in the embankment, and investigated changes in soil properties. As a result, we showed that (1) the velocities of S and P waves in the upper section were 449 and 993 m/s, respectively, prior to the strong earthquake motions derived from earthquake records from January 1997 through October 2001, whereas 608 and 1538, respectively, in the lower section, (2) in the Iwate–Miyagi Nairiku earthquake, the S-wave velocity in the upper section decreased to 158 m/s in the principal shock, and (3) in subsequent minor earthquakes the propagation velocity increased more or less in proportion with the logarithm of the number of elapsed days, requiring three years or longer to return to the initial value, (4) although similar changes were observed in the Great East Japan earthquake of 2011, the reduction in propagation velocity that remained after the principal shock was smaller than in the case of the Iwate–Miyagi Nairiku earthquake, and it was judged that there were no large effects on the dam body such as those that occurred in the Iwate–Miyagi Nairiku earthquake, and furthermore (5) in the principal shock of the Iwate–Miyagi Nairiku earthquake, the shear modulus in the upper part of the dam body decreased from 400 to 50 MPa (with a maximum shear strain of 10−3), resulting in more pronounced changes than in the lower section, whereas the damping ratio increased by at least 10% in the lower section during the principal shock of the Iwate–Miyagi Nairiku earthquake, resulting in much greater changes than in the upper section.

Synthetic Aperture Radar Image Despeckling Based on Multi-Weighted Sparse Coding

Synthetic aperture radar (SAR) images are inherently degraded by speckle noise caused by coherent imaging, which may affect the performance of the subsequent image analysis task. To resolve this problem, this article proposes an integrated SAR image despeckling model based on dictionary learning and multi-weighted sparse coding. First, the dictionary is trained by groups composed of similar image patches, which have the same structural features. An effective orthogonal dictionary with high sparse representation ability is realized by introducing a properly tight frame. Furthermore, the data-fidelity term and regularization terms are constrained by weighting factors. The weighted sparse representation model not only fully utilizes the interblock relevance but also reflects the importance of various structural groups in despeckling processing. The proposed model is implemented with fast and effective solving steps that simultaneously perform orthogonal dictionary learning, weight parameter updating, sparse coding, and image reconstruction. The solving steps are designed using the alternative minimization method. Finally, the speckles are further suppressed by iterative regularization methods. In a comparison study with existing methods, our method demonstrated state-of-the-art performance in suppressing speckle noise and protecting the image texture details.

Sparse-View Neutron CT Reconstruction Using a Modified Weighted Total Difference Minimization Method

Indirect neutron imaging is an effective method for nondestructive testing of spent nuclear fuel elements. Considering the difficulty of obtaining experimental data in a high-radiation environment and the characteristic of high noise of neutron images, it is difficult to use the traditional FBP algorithm to recover the complete information of the sample based on the limited projection data. Therefore, it is necessary to develop the sparse-view CT reconstruction algorithm for indirect neutron imaging. In order to improve the quality of the reconstruction image, an iterative reconstruction method combining SIRT, MRP, and WTDM regularization is proposed. The reconstruction results obtained by using the proposed method on simulated data and actual neutron projection data are compared with the results of four other algorithms (FBP, SIRT, SIRT-TV, and SIRT-WTDM). The experimental results show that the SIRT-MWTDM algorithm has great advantages in both objective evaluation index and subjective observation in the reconstruction image of simulated data and neutron projection data.

Composition of the lunar mantle for lower mantle high-velocity seismic model

We investigated models of the internal structure of initially homogeneous Moon differentiated as a result of partial melting, using data on seismic velocities according to the seismic models assume the zonal structure of the lunar mantle is a model of the Moon which was obtained with using the array processing methods of high velocities in the lower mantle. As a result of inversion of gravity (mass, moment of inertia), seismic (P- and S-waves velocities) and petrological (balance ratios) data, the Monte Carlo method was used to reconstruct the chemical composition and internal structure of the Moon. The phase composition and physical properties of the mantle were obtained with Gibbs free energy minimization method and equations of state in the five-component system CaO-FeO-MgO-Al2O3-SiO2. For all models, possible values of seismic velocities and concentrations of the main oxides in three zones of the mantle were obtained, satisfying the geochemical and geophysical constraints and the possible sizes of the Fe-10%S core were determined. It was found that the lunar mantle chemical composition (concentration of FeO, Al2O3 and CaO) differs depending on the mantle zone. Constraints on the values of seismic velocities in the lower mantle and the most probable size of the lunar core were determined: VP ≤ 8.45 km/s; Fe-10%S core radius is ∼360 km.

Efficiency of Ni Content in Laterite Nickel Deposits through The Least Square Method Approach on Semivariogram

Ordinary kriging is one of the geostatistical techniques used for spatial prediction on a spatially distributed random plane. Ordinary kriging is a linear unbiased estimator which is part of a semivariogram system of equations that minimizes errors of variance in estimating mineral resources. The semivariogram model shows optimal results in the estimation using the least square method, the effective minimization method smoothes the data points against the curve on a semivariogram graph, the least square makes the size error efficient in the semivariogram model and has been proven to be effective in reducing errors in the semivariogram model in the case of laterite nickel deposits. at PT. Vale Indonesia Tbk. Thus, conclusively the prediction of unsampled Ni content results is very accurate. This is indicated by the lowest root mean square error (RMSE) in limonite in the exponential model, saprolite in the spherical model, and bedrock in the gaussian model. The greatest value of Ni content in this study was in the saprolite layer.

Minimization Method for 3D Surface Roughness Evaluation Area

3D surface roughness measurement is still a less mature procedure than its 2D version. The size of the evaluation area is not as standardized as the measurement length in the 2D version. The purpose of this study is to introduce a method for minimizing the evaluated surface area. This could help industrial applications in minimizing the time and cost of measurements. Machining experiments (hard turning and infeed grinding) and surface roughness measurements were carried out for automotive industrial parts to demonstrate the introduced method. Some frequently used roughness parameters were analyzed. Basic statistical calculations were applied to analyze the relationship between the surface area and the roughness parameter values and regression analyses were applied to validate the results in case of the applied technological data. The main finding of the study is that minimum evaluation areas can be clearly designated and, depending on the different roughness parameter–procedure version, different evaluation sizes (Sa: 1.3 × 1.3 mm; Sq: 1.4 × 1.4 mm; Ssk and Sku: 2 × 2 m; Sp and Sv: 1.7 × 1.7 mm) are recommended.

Relationship between Starting Torque and Thermal Behaviour for a Permanent Magnet Synchronous Generator (PMSG) Applied with Vertical Axis Wind Turbine (VAWT)

The application of wind turbine technology in low wind speed regions such as Southeast Asia has recently attracted increased attention. Wind turbines are designed as special structures with low starting torque, and many starting torque minimization processes exist for permanent magnet synchronous generators (PMSGs). Plurality is applied to decrease the starting torque in radial flux permanent magnet disk generators. The most popular starting torque minimization method uses a magnet skew technique. When used at 20°, this technique reduced starting torque by 4.72% (on load) under 500 rpm at 50 Hz for 120 min. By contrast, a PMSG with magnet skew conditions set at under 2° reduced electrical power by 3.86%. For high-speed PMSGs, magnet skew techniques affect the generation of heat in the coils (stator), with heat decrease at the middle of the coil, on its surface and between the coils at 2.90%, 3.10% and 2.40%, respectively. PMSGs were installed in vertical axis wind turbines (VAWTs), and heat generation in relation to wind speed and electrical power was assessed. Magnet skew techniques can be used in PMSGs to reduce staring torque, while skew techniques also reduce electrical power and heat generated at the stator.