5 research outputs found

    PERILAKU TEKANAN AIR PORI PADA BENDUNGAN BAJULMATI MULAI PENGISIAN HINGGA TAHUN 2020

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    Bendungan Bajulmati mulai pengisian awal waduk pada tahun 2015 dan pemantauan tekanan air pori dilakukan dengan pengukuran pisometer pneumatik yang dipasang di dalam tubuh dan pondasi bendungan. Pemantauan rutin dan interpretasi yang baik dari data pembacaan pisometer yang terpasang pada bendungan perlu dilakukan guna mengetahui perilaku tekanan air pori pada bendungan, sehingga dapat mengetahui secara dini masalah yang mungkin sedang terjadi, mengingat bahwa tekanan air pori dapat mempengaruhi keamanan stabilitas lereng. Tujuan dari penelitian ini adalah untuk mendapatkan perilaku tekanan air pori pada Bendungan Bajulmati mulai dari pengisian awal waduk hingga tahun 2020. Perilaku tekanan air pori pada Bendungan Bajulmati, dilakukan dengan pemodelan SEEP/W kondisi transient, kemudian dibandingkan dengan data hasil pengukuran pisometer di lapangan. Hasil pemodelan menunjukkan bahwa muka air waduk mempengaruhi perilaku tekanan air pori pada tubuh bendungan. Nilai tekanan air pori hasil pemodelan nilainya lebih rendah daripada pengukuran pisometer di lapangan dengan deviasi sebesar 2,2 dan 2,1 mH2O, namun demikian data pembacaan pisometer PP6 dan PP7 masih menunjukkan trend yang sama dengan hasil pemodelan, sehingga disimpulkan bahwa kondisinya masih wajar

    Optimal quantization of the mean measure and application to clustering of measures

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    This paper addresses the case where data come as point sets, or more generally as discrete measures. Our motivation is twofold: first we intend to approximate with a compactly supported measure the mean of the measure generating process, that coincides with the intensity measure in the point process framework, or with the expected persistence diagram in the framework of persistence-based topological data analysis. To this aim we provide two algorithms that we prove almost minimax optimal. Second we build from the estimator of the mean measure a vectorization map, that sends every measure into a finite-dimensional Euclidean space, and investigate its properties through a clustering-oriented lens. In a nutshell, we show that in a mixture of measure generating process, our technique yields a representation in Rk\mathbb{R}^k, for k∈N∗k \in \mathbb{N}^* that guarantees a good clustering of the data points with high probability. Interestingly, our results apply in the framework of persistence-based shape classification via the ATOL procedure described in \cite{Royer19}

    Optimal quantization of the mean measure and applications to statistical learning

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    This paper addresses the case where data come as point sets, or more generally as discrete measures. Our motivation is twofold: first we intend to approximate with a compactly supported measure the mean of the measure generating process, that coincides with the intensity measure in the point process framework, or with the expected persistence diagram in the framework of persistence-based topological data analysis. To this aim we provide two algorithms that we prove almost minimax optimal. Second we build from the estimator of the mean measure a vectorization map, that sends every measure into a finite-dimensional Euclidean space, and investigate its properties through a clustering-oriented lens. In a nutshell, we show that in a mixture of measure generating process, our technique yields a representation in Rk\mathbb{R}^k, for k∈N∗k \in \mathbb{N}^* that guarantees a good clustering of the data points with high probability. Interestingly, our results apply in the framework of persistence-based shape classification via the ATOL procedure described in \cite{Royer19}
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