Silicon-compatible high-hole-mobility transistor with an undoped germanium channel for low-power application

In this work, Ge-based high-hole-mobility transistor with Si compatibility is designed, and its performance is evaluated. A 2-dimensional hole gas is effectively constructed by a AlGaAs/Ge/Si heterojunction with a sufficiently large valence band offset. Moreover, an intrinsic Ge channel is exploited so that high hole mobility is preserved without dopant scattering. Effects of design parameters such as gate length, Ge channel thickness, and aluminum fraction in the barrier material on device characteristics are thoroughly investigated through device simulations. A high on-current above 30 ??A/??m along with a low subthreshold swing was obtained from an optimized planar device for low-power applications.open0

Search for Optically Dark Infrared Galaxies without Counterparts of Subaru Hyper Suprime-Cam in the AKARI North Ecliptic Pole Wide Survey Field

We present the physical properties of AKARI sources without optical counterparts in optical images from the Hyper Suprime-Cam (HSC) on the Subaru telescope. Using the AKARI infrared (IR) source catalog and HSC optical catalog, we select 583 objects that do not have HSC counterparts in the AKARI North Ecliptic Pole wide survey field (~5 deg2). Because the HSC limiting magnitude is deep (gAB ~ 28.6), these are good candidates for extremely red star-forming galaxies (SFGs) and/or active galactic nuclei (AGNs), possibly at high redshifts. We compile multiwavelength data out to 500 μm and use them for fitting the spectral energy distribution with CIGALE to investigate the physical properties of AKARI galaxies without optical counterparts. We also compare their physical quantities with AKARI mid-IR selected galaxies with HSC counterparts. The estimated redshifts of AKARI objects without HSC counterparts range up to z ~ 4, significantly higher than for AKARI objects with HSC counterparts. We find that (i) 3.6 – 4.5 μm color, (ii) AGN luminosity, (iii) stellar mass, (iv) star formation rate, and (v) V-band dust attenuation in the interstellar medium of AKARI objects without HSC counterparts are systematically larger than those of AKARI objects with counterparts. These results suggest that our sample includes luminous, heavily dust-obscured SFGs/AGNs at z ~ 1–4 that are missed by previous optical surveys, providing very interesting targets for the coming era of the James Webb Space Telescope

Impact of Deep Learning Optimizers and Hyperparameter Tuning on the Performance of Bearing Fault Diagnosis

Deep learning has recently resulted in remarkable performance improvements in machine fault diagnosis using only raw input vibration signals without signal preprocessing. However, research on machine fault diagnosis using deep learning has primarily focused on model architectures, even though optimizers and their hyperparameters used for training can have a significant impact on model performance. This paper presents extensive benchmarking results on the tuning of optimizer hyperparameters using various combinations of datasets, convolutional neural network (CNN) models, and optimizers with varying batch sizes. First, we set the hyperparameter search space and then trained the models using hyperparameters sampled from a quasi-random distribution. Subsequently, we refined the search space based on the results of the first step and finally evaluated model performances using noise-free and noisy data. The results showed that the learning rate and momentum factor, which determine training speed, substantially affected the model’s accuracy. We also discovered that the impacts of batch size and model training speed on model performance were highly correlated; large batch sizes led to higher performances at higher learning rates or momentum factors. Conversely, model performances tended to be high for small batch sizes at lower learning rates or momentum factors. In addition, regarding the growing attention to on-device artificial intelligence (AI) solutions, we assessed the accuracy and computational efficiency of candidate models. A CNN with training interference (TICNN) was the most efficient model in terms of computational efficiency and robustness against noise among the benchmarked candidate models

Search Space Reduction for Determination of Earthquake Source Parameters Using PCA and k-Means Clustering

The characteristics of an earthquake can be derived by estimating the source geometries of the earthquake using parameter inversion that minimizes the L2 norm of residuals between the measured and the synthetic displacement calculated from a dislocation model. Estimating source geometries in a dislocation model has been regarded as solving a nonlinear inverse problem. To avoid local minima and describe uncertainties, the Monte-Carlo restarts are often used to solve the problem, assuming the initial parameter search space provided by seismological studies. Since search space size significantly affects the accuracy and execution time of this procedure, faulty initial search space from seismological studies may adversely affect the accuracy of the results and the computation time. Besides, many source parameters describing physical faults lead to bad data visualization. In this paper, we propose a new machine learning-based search space reduction algorithm to overcome these challenges. This paper assumes a rectangular dislocation model, i.e., the Okada model, to calculate the surface deformation mathematically. As for the geodetic measurement of three-dimensional (3D) surface deformation, we used the stacking interferometric synthetic aperture radar (InSAR) and the multiple-aperture SAR interferometry (MAI). We define a wide initial search space and perform the Monte-Carlo restarts to collect the data points with root-mean-square error (RMSE) between measured and modeled displacement. Then, the principal component analysis (PCA) and the k-means clustering are used to project data points with low RMSE in the 2D latent space preserving the variance of original data as much as possible and extract k clusters of data with similar locations and RMSE to each other. Finally, we reduce the parameter search space using the cluster with the lowest mean RMSE. The evaluation results illustrate that our approach achieves 55.1~98.1% reductions in search space size and 60~80.5% reductions in 95% confidence interval size for all source parameters compared with the conventional method. It was also observed that the reduced search space significantly saves the computational burden of solving the nonlinear least square problem

Parallel Dislocation Model Implementation for Earthquake Source Parameter Estimation on Multi-Threaded GPU

Graphics processing units (GPUs) have been in the spotlight in various fields because they can process a massive amount of computation at a relatively low price. This research proposes a performance acceleration framework applied to Monte Carlo method-based earthquake source parameter estimation using multi-threaded compute unified device architecture (CUDA) GPU. The Monte Carlo method takes an exhaustive computational burden because iterative nonlinear optimization is performed more than 1000 times. To alleviate this problem, we parallelize the rectangular dislocation model, i.e., the Okada model, since the model consists of independent point-wise computations and takes up most of the time in the nonlinear optimization. Adjusting the degree of common subexpression elimination, thread block size, and constant caching, we obtained the best CUDA optimization configuration that achieves 134.94×, 14.00×, and 2.99× speedups over sequential CPU, 16-threads CPU, and baseline CUDA GPU implementation from the 1000×1000 mesh size, respectively. Then, we evaluated the performance and correctness of four different line search algorithms for the limited memory Broyden–Fletcher–Goldfarb–Shanno with boundaries (L-BFGS-B) optimization in the real earthquake dataset. The results demonstrated Armijo line search to be the most efficient one among the algorithms. The visualization results with the best-fit parameters finally derived by the proposed framework confirm that our framework also approximates the earthquake source parameters with an excellent agreement with the geodetic data, i.e., at most 0.5 cm root-mean-square-error (RMSE) of residual displacement