科学研究費補助金 研究種目:試験研究 課題番号:61850069 研究代表者:日比野 倫夫 研究期間:1986-1987年度

科学研究費補助金 研究種目:試験研究(B)(1) 課題番号:05555158 研究代表者:谷川 恭雄 研究期間:1993-1994年度

従来型の繊維強化プラスチック(FRP)に対し環境負荷の少ない「グリーンコンポジット」の実用的な成形プロセスの確立が切望されている。本研究では、天然繊維およびポリ乳酸を原料とする長繊維強化樹脂(LFT)ペレット製造および射出成形による複合材製造法を対象に、複合材の力学的特性に対する繊維の含有水分や熱劣化の影響、繊維分散の効果等を把握し、高い特性を発揮させるための成形プロセスおよび諸条件を提示した。 / 'Green Composites' have been attracting attention due to their high sustainability and carbon neutrality. This study investigated the preparation process for composites of long jute fiber reinforced polylactic acid by LFT-pellet manufacturing method followed by injection molding. The author explored effect of several factors, such as moisture in fiber, heat decomposition of fiber at processing and the level of fiber dispersion in matrix resin, on mechanical properties of composites. The author eventually proposed the optimized process and operating windows for attaining higher mechanical properties of composites. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

金融市場是個變化莫測的環境，看似隨機，在隨機中卻隱藏著某些特性與關係。不論是自然現象中的氣象預測或是金融領域中對下一時刻價格的預測, 都有相似的複雜性。 時間序列的預測一直都是許多領域中重要的項目之一, 金融時間序列的預測也不例外。在本論文中我們針對金融時間序列的非線性與非穩態關係引入類神經網路(ANNs) 與集合經驗模態分解法(EEMD), 藉由ANNs處理非線性問題的能力與EEMD處理時間序列信號的優點，並進一步與傳統上使用於金融時間序列分析的自回歸滑動平均模型(ARMA)進行複合式的模型建構，引入燭型圖概念嘗試進行高頻下的台指期貨TAIEX交易。在不計交易成本的績效測試下本研究的高頻交易模型有突出的績效，證明以ANNs、EEMD方法與ARMA組成的混合式模型在高頻時間尺度交易下有相當的發展潛力，具有進一步發展的價值。在處理高頻時間尺度下所產生的大型數據方面，引入平行運算架構SPMD(single program, multiple data)以增進其處理大型資料下的運算效率。本研究亦透過分析高頻時間尺度的本質模態函數(IMFs)探討在高頻尺度下影響台指期貨價格的因素。 / Financial market is complex, unstable and non-linear system, it looks like have some principle but the principle usually have exception. The forecasting of time series always an issue in several field include finance. In this thesis we propose several version of hybrid models, they combine Ensemble Empirical Mode Decomposition (EEMD), Back-Propagation Neural Networks(BPNN) and ARMA model, try to improve the forecast performance of financial time series forecast. We also found the physical means or impact factors of IMFs under high-frequency time-scale. For processing the massive data generated by high-frequency time-scale, we pull in the concept of big data processing, adopt parallel computing method ”single program, multiple data (SPMD)” to construct the model improve the computing performance. As the result of backtesting, we prove the enhanced hybrid models we proposed outperform the standard EEMD-BPNN model and obtain a good performance. It shows adopt ANN, EEMD and ARMA in the hybrid model configure for high-frequency trading modeling is effective and it have the potential of development.

多数の溶媒分子に取り囲まれている溶液中において溶質分子の化学反応素過程を考える場合、溶媒分子による反応の平衡論的、動的な効果を考える必要がある。本研究では、ユニークな反応場として水や有機溶媒とは区別される、超臨界流体とイオン液体をとり上げ、溶質分子のプロトン移動反応、光解離反応について、時間分解レーザー分光と分子動力学計算、理論的解析を行い、その現象を明らかにする試みをおこなった。 / In solution, solvent molecules involve chemical reaction of solute molecules and could alter both reaction yield and kinetics. In this thesis, the author focused on fundamental chemical reactions (intermolecular proton transfer and photodissociate reaction) in a unique class of solvents, supercritical fluids and ionic liquids. By measuring time-resolved fluorescence spectrum and transient absorption spectrum of solutes, the author discusses how the reaction yield and kinetics are described by solvent physicochemical properties. / 博士(理学) / Doctor of Philosophy in Science / 同志社大学 / Doshisha University