自由曲線に関して，κ-曲線の提案により，2次曲線についての研究が活発化している．本研究では，多項式2次曲線，有理2次曲線，双曲曲線，splines in tension等を含む3個の制御点で定義される自由曲線について以下に述べる形状一意性定理を証明する．形状一意性定理tをパラメータとし，3個の制御点に対する混ぜ合わせ関数をu(t), v(t), およびw(t)とするとき，v(t)2 = α u(t) w(t) for t in [0,1]が成り立つとき，曲線形状はαにより一意に決定される．
建造物の効率的な維持管理作業のために，重複を持つ画像集合から3次元as-isモデルを生成可能なStructure from MotionとMulti-View Stereo (SfM-MVS)技術の活用が広がっている．しかし，どのカメラポーズで画像を撮影するかを事前に推定することが困難なため，生成されるモデルの品質が低下する場合がある．本研究ではSfM-MVSによる効率的で高品質なas-isモデル生成のための最適撮影計画支援システムの開発を最終目的とし，本報では，既報で提案した品質予測指標値に基づいて，追加撮影を終了するための条件を検討したので報告する．
Recognition of wave-dissipating blocks stacked on the coast is a significant and challenging task. This task consists of semantic segmentation, instance segmentation, and precise 6D pose estimation. The convolutional neural network (CNN) trained by the block fall simulation is used to classify the original point cloud with multiple types into the single one and to partition into the subsets corresponding both to the individual blocks. A descriptor-based method is then used for the 6D pose estimation of the block. We evaluated our method on several scenes containing wave-clipping blocks with multiple block types. The experimental results show that our method is effective and efficient.
Injection molded direct joining is a promising technique to fabricate metal-plastic direct joints. The successful joining is due to the infiltration of melted plastic into surface structures of metal plates. Thus, the injection parameters, which control the plastic flow, have great influence on the joining strength. It is desirable to test every combination of injection parameters to get a full glimpse of their influence on the joining strength. However, this desire would require several hundreds of experiments, which is practically difficult. Here, we tried to achieve this desire by using Taguchi method and machine learning. Metal-plastic joints of a 30% glass fiber reinforced polybutylene terephtalate (PBT) and hot water treated aluminum alloy (A5052) plates are used. At first, experiments were designed with Taguchi method and performed to prepare data for machine learning. Then, the data were used to train a back propagation neural network model. The model was then used to predict the joining strength of every combination of injection parameters. The results show that injection parameters have strong interaction with each other. A condition of high packing pressure and low injection speed will produce higher joining strength.
Injection molded direct joining (IMDJ) is one type of metal-plastic direct joining method which first treats the metal surface and then injects melted polymer onto the surface via injection molding technology. IMDJ excellently suitable for mass production environment for the characteristics of injection molding but has not been widely applied because its performance, such as joining strength, is not high enough. In this study, we focused on joining performance improving by mixing proper additives (flow modifier OSGOL MF-11) in the engineering polymer polyamide 6 (PA6). We studied the influence of the amounts of additives on the joining strength. It reveals that mixing additives in PA6 is a feasible method to improve the joining strength and found that the best joining strength increased up to 75%. The mechanical interlocking improvement by higher fluidity is confirmed by cross-sectional analysis. The results of FTIR spectra analysis show the possibility of hydrogen bonding occurred which contributing to the joining strength. The results clearly show that polymer additive feasibility enhances the joining performance, which improves the prospects for further applications of IMDJ technology in the future.