Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature generator network to mimic the discriminator. Two problems exist with these methods. First, the domain classifier only tries to distinguish the features as a source or target and thus does not consider task-specific decision boundaries between classes. Therefore, a trained generator can generate ambiguous features near class boundaries. Second, these methods aim to completely match the feature distributions between different domains, which is difficult because of each domain's characteristics. To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries. We propose to maximize the discrepancy between two classifiers' outputs to detect target samples that are far from the support of the source. A feature generator learns to generate target features near the support to minimize the discrepancy. Our method outperforms other methods on several datasets of image classification and semantic segmentation. The codes are available at \url{https://github.com/mil-tokyo/MCD_DA}Comment: Accepted to CVPR2018 Oral, Code is available at https://github.com/mil-tokyo/MCD_D

On the Initial Imperfections and Their Relations to the Strength of Webplates of Actual Steel Bridges

This study is mainly concerned with statistical investigations on the actual initial imperfections and their relations to the load carrying capacities of the webplates of steel bridges. The basic data to be used throughout the study have been collected by the Initial Deflection Measurement Committee, IDMC, of the Society of Steel Construction of Japan, abbreviated as JSSC. In order to show what types of webplate systems are covered throughout the study, various statistical information on the mechanical and geometric parameters is firstly presented, and then, various kinds of the initial imperfections existing in the actual steel bridges are subjected to statistical considerations. Through the statistical analyses, the probabilities of exceedance of various imperfections are evaluated. From these fractile values, some comments on the regulations of the initial imperfections by the current design codes are drawn

連立1次方程式の直接解法とソフトウェア

1.連立1次方程式の直接解法 2.一般行列に対する直接解法ソフトウェア 3.疎行列に対する直接法 4.WSMPの性能評

Analysis of Seepage through Embankment Dam with Central Core

中心コア型フィルダムの浸透流解析をFEMにより行った. まず,中心コア型フィルダムの浸透流解析の方法を示し,その解析結果と中心コア内のみの解析結果とを比較し,中心コア内のみの解析によって十分な精度で設計が行えることを示した. 次に,中心コアの形状を変化させて解析を行い異方性を考慮した浸透流量を容易に求める方法を示した. また,浸出点の位置および最大動水勾配に関しても考察を加えた. 等体積のコアの場合はコア底巾を広く,クレスト巾を狭くする方が最大動水勾配は小さくなり,浸透流量も少なくなるが浸出点の位置は高くなることを示した. 次に,CASAGRANDEの方法による値とFEM解との比較を行い,浸透流量および浸出点の位置には大きな相違があり,しかも,CASAGRANDEの方法は危険側の値を与えることを示した

Two Dimensional Analysis of Non-Darcy Flow

二次元非線形浸透流の有限要素法による解析法およびダム底中央に矢板が存在する場合の浸透特性を示した. FORCHHEIMER則の場合は定数aが小さい程,また,bが大きい程線形流との相違が大きくなることを示した. この解法は自由水面を有する場合にも容易に応用することが可能である. 現在応力計算における材料特性,すなわち,応力－ひずみ関係が問題にされているように,浸透流解析においても動水勾配－流速関係のデータの更なる蓄積が急がれるものである

Three Dimensional Seepage Analysis through Earth Dam : Rapid Drawdown Seepage Analysis

本論文においては数値計算によるアースダム堤体内の水位急降下時における三次元浸透特性を示す. 上下流のり面勾配,およびアバットメント勾配が1.5割のダムタイプを設定した. 浸水係数としては水平方向浸水係数Kx,Ky鉛直方向浸水係数をK2としてKx=Ky＝Kz=1.0,Kz=Ky=1.0,Kz=10.0という二つのケースを考えた. かつ,水位が0.5.0急低下する場合を設定した. そして,各ケースにおけるポテンシャル分布,その勾配,および流速分布に関する特性を詳細に示した