Standardized Benchmark Dataset for Localized Exposure to a Realistic Source at 10$-$90 GHz

要約

タイトル：10$-$90 GHzにおける実現可能なソースによる局所暴露に対する標準化ベンチマークデータセット

要約：
– 無料で利用可能な標準化されたデータセットが欠けていることは、露光評価および測定研究における新しい計算技術の開発とテストの進行を妨げる要因となっている。
– これに対処するために、10$-$90 GHzでのさまざまな安定した条件下での暴露シナリオを考慮した、高精度の数値データからなる包括的でオープンソースのデータセットが提供されている。
– さらに、これらのデータから最大温度上昇を予測するための代替モデルが提供され、最も妥当な予測性能を示した。

要約(オリジナル)

The lack of freely available standardized datasets represents an aggravating factor during the development and testing the performance of novel computational techniques in exposure assessment and dosimetry research. This hinders progress as researchers are required to generate numerical data (field, power and temperature distribution) anew using simulation software for each exposure scenario. Other than being time consuming, this approach is highly susceptible to errors that occur during the configuration of the electromagnetic model. To address this issue, in this paper, the limited available data on the incident power density and resultant maximum temperature rise on the skin surface considering various steady-state exposure scenarios at 10$-$90 GHz have been statistically modeled. The synthetic data have been sampled from the fitted statistical multivariate distribution with respect to predetermined dosimetric constraints. We thus present a comprehensive and open-source dataset compiled of the high-fidelity numerical data considering various exposures to a realistic source. Furthermore, different surrogate models for predicting maximum temperature rise on the skin surface were fitted based on the synthetic dataset. All surrogate models were tested on the originally available data where satisfactory predictive performance has been demonstrated. A simple technique of combining quadratic polynomial and tensor-product spline surrogates, each operating on its own cluster of data, has achieved the lowest mean absolute error of 0.058 {\deg}C. Therefore, overall experimental results indicate the validity of the proposed synthetic dataset.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI