Bayesian modeling for composite reliability and maximal reliability.

A reliability coefficient in psychometrics is used as an index of consistency. The α coefficient has been widely used as an estimate of reliability coefficient: however, in recent years, there has been an increasing interest in devising other methods of estimating reliability. I have made extensive revisions to enhance clarity and reduce redundancy. In addition to reporting the point estimate of the reliability coefficient, it is also recommended to report the results of interval estimation. Furthermore, psychological research using Bayesian modeling is gradually gaining popularity. In this paper, we introduce a Bayesian model for obtaining the point and interval estimation of maximal reliability and ω coefficient using a statistical analysis environment R and Stan that implements HMC sampling.信頼性係数は心理尺度開発場面で、尺度の安定の度合いを示す指標として利用されている。信頼性係数の代表的な指標としてα係数が広く利用されてきた。近年、α係数の再検討が進み、その他の信頼性係数の指標にも関心が高まっている。また、信頼性係数の報告も点推定値のみならず、区間推定を行った結果を報告する事も意識されるようになっている。更に、ベイズモデリングを利用した心理学研究が増えつつある。本稿では統計解析環境RおよびHMCサンプリングを実装したStanを用いて、ベイズモデリングによって最大信頼性およびω係数の推定値と確信区間を構成する方法を紹介する

Finite element analysis of tube drawing process with diameter expansion

This paper presents a tube drawing process with diameter expansion for producing a thin-walled tube effectively. In this proposed process, the tube was flared by a plug pushing into the tube, and then the tube was expanded by drawing the plug in the tube axial direction with chucking the flared tube edge. Optimum plug shape, such as the plug half angle and the corner radius, was investigated by a series of analyses using the finite element method (FEM) for improving the forming limit and the dimension accuracy. At first, a friction coefficient was determined to 0.3 by a comparison of the flaring limit between the analysis and the experiment of the tube flaring. As a result of the analyses in the drawing with the diameter expansion, the forming limit was high when the plug half angle was set to 18~30°. The thickness reduction ratio increased with an increase in the expansion ratio and the plug half angle. In addition, the overshoot, which is a difference between the plug diameter and the tube inner diameter after the drawing, was prevented by using the plug with the corner radius of 20 mm

Estimation of response of steel sheet plated with thin hard layer

Elastic and elastic-plastic responses were examined of cantilevers made from a cold rolled steel sheet and made from the same sheet plated with a thin hard layer. Tension test of these sheets showed a non-linear behaviour even in the area of small strain and conventional linear theory of cantilever had to be modified. By extending this theory to a sheet plated with a thin hard layer Young’s modulus of plated layer was estimated. The range of estimated Young’s modulus was similar to those in previous works but material non-linearity, especially on the compression side, must be measured more precisely

Experimental and numerical analyses on the characteristics of twin skew rolling

Elastic-plastic FEA was carried out on the rolling process of twin skew rolling for a blooming mill to evaluate the influences of roll diameter, skew angle, and coefficient of friction on the suppression effect of porosities in the vicinity of centre axis of the material. Rolling by using a proto-type mill and modelling clay was then carried out to verify the validity of numerical analysis. Both results showed that the larger the roll diameter, and also the larger the coefficient of friction, the higher the suppression effect of porosities

Significance of High-frequency Electrical Brain Activity

　Electroencephalogram (EEG) data include broadband electrical brain activity ranging from infra-slow bands (200 / 250 Hz, respectively) are particularly of note due to their very close relationship to epileptogenicity, with the possibility that they could function as a surrogate biomarker of epileptogenicity. In contrast, physiological high-frequency activity plays an important role in higher brain functions, and the differentiation between pathological / epileptic and physiological HFOs is a critical issue, especially in epilepsy surgery. HFOs were initially recorded with intracranial electrodes in patients with intractable epilepsy as part of a long-term invasive seizure monitoring study. However, fast oscillations (FOs) in the ripple and gamma bands (40-80 Hz) are now noninvasively detected by scalp EEG and magnetoencephalography, and thus the scope of studies on HFOs /FOs is rapidly expanding

VARIATIONAL PROBLEMS FOR INTEGRAL INVARIANTS OF THE SECOND FUNDAMENTAL FORM OF A MAP BETWEEN PSEUDO-RIEMANNIAN MANIFOLDS

We study variational problems for integral invariants, which are defined as integrations of invariant functions of the second fundamental form, of a smooth map between pseudo-Riemannian manifolds. We derive the first variational formulae for integral invariants defined from invariant homogeneous polynomials of degree two. Among these integral invariants, we show that the Euler–Lagrange equation of the Chern–Federer energy functional is reduced to a second order PDE. Then we give some examples of Chern–Federer submanifolds in Riemannian space forms

Prediction System of Cloud Distribution Image Using Fully Convolutional Networks

In this paper, we propose a cloud distribution prediction model in which fully convolutional networks are used to improve the prediction accuracy for photovoltaic power generation systems. The model learns the cloud distribution from meteorological satellite images and predicts the cloud image 60 min later. We examined the applicability of Day Microphysics RGB as input to the cloud image prediction model. Day Microphysics RGB is a type of RGB composite image based on the observation image of Himawari-8. It is used for daytime cloud analysis and can perform detailed cloud analysis, for example, the discrimination of cloud areas such as upper and lower clouds. The performance of the proposed method is evaluated on the basis of the root mean square error of the prediction and ground truth images

Artificial Intelligence-based Detection of Epileptic Discharges from Pediatric Scalp Electroencephalograms: A Pilot Study

We developed an artificial intelligence (AI) technique to identify epileptic discharges (spikes) in pediatric scalp electroencephalograms (EEGs). We built a convolutional neural network (CNN) model to automatically classify steep potential images into spikes and background activity. For the CNN model’ training and validation, we examined 100 children with spikes in EEGs and another 100 without spikes. A different group of 20 children with spikes and 20 without spikes were the actual test subjects. All subjects were ≥ 3 to 0.97 when referential and combination EEG montages were used, and 0.99, indicating high performance of the classification method. EEG patterns that interfered with correct classification included vertex sharp transients, sleep spindles, alpha rhythm, and low-amplitude ill-formed spikes in a run. Our results demonstrate that AI is a promising tool for automatically interpreting pediatric EEGs. Some avenues for improving the technique were also indicated by our findings