健診データ処理システムの構築

要旨ありヘルスサイエンスと統計科学研究詳

Estimation of X-ray Energy Spectrum of Cone-Beam Computed Tomography Scanner Using Percentage Depth Dose Measurements and Machine Learning Approach

This study presents, for the first time, a method to indirectly estimate the cone-beam computed tomography (CBCT) x-ray spectrum in the diagnostic energy range from the percentage depth dose (PDD) using machine learning (ML) algorithms. Assuming that the measured PDD is a weighted mean of monochromatic PDDs (mPDDs) resulting from monochromatic x-ray energies, mPDDs from the diagnostic energy range of 10 to 140 keV are simulated at 1 keV intervals by Monte Carlo (MC) calculation. Then, x-ray spectrum prediction models are constructed using two different ML approaches, namely the artificial neural network (ANN) based on a generative model and a maximum a posterior (MAP) model. Both models account for more than 80% of the x-ray photons obtained by full MC simulations in commercial CBCT systems. The present method is expected to be applied into a beam hardening reduction in CBCT reconstruction, CBCT dose calculation, and a material decomposition which require exact information on the x-ray energy spectrum

In-materio reservoir working at low frequencies in a Ag2S-island network

A Ag2S-island network is fabricated with surrounding electrodes to enable it to be used as a reservoir for unconventional computing. Local conductance change occurs due to the growth/shrinkage of Ag filaments from/into each Ag2S island in the reservoir. The growth/shrinkage of Ag filaments is caused by the drift of Ag+ cations in each Ag2S island, which results in a unique non-linear response as a reservoir, especially at lower frequencies. The response of the reservoir is shown to depend on the frequency and amplitude of the input signals. So as to evaluate its capability as a reservoir, logical operations were performed using the subject Ag2S-island network, with the results showing an accuracy of greater than 99%

Report on 2020 International Conference on Emerging Technologies for Communications (ICETC 2020)

The IEICE Communications Society hosted the 2020 International Conference on Emerging Technologies for Communications (hereinafter referred to as “ICETC2020”) online from December 2-4, 2020 [1]. Figures 1 and 2 present opening ceremony on Dec. 2 and reception on Dec. 4, respectively. This conference was the first flagship international conference organized by a Society in IEICE. The conference was attended by 589 participants including many students