3 research outputs found
Simulasi Energi dan Keekonomian Sistem Pembangkit Listrik Tenaga Surya (PLTS) untuk Fungsi Peak Load Shaving pada Bangunan di Lingkungan Kampus ITB
Pada paper ini, dilakukan simulasi produksi energi dan keekonomian dari sistem PLTS terintegrasi dengan jaringan listrik yang merupakan bagian dari proyek instalasi sistem smart microgrid di bangunan Center for Advanced Sciences (CAS) ITB. Terdapat dua sistem PLTS yang dianalisis yaitu sistem PLTS 40 kWp tanpa baterai, dan sistem PLTS 10 kWp dengan baterai yang terhubung ke beban kritis. Kedua sistem PLTS tersebut dirancang dengan tujuan peak load shaving yang dapat meminimalisir adanya ketidakseimbangan antara produksi energi surya dan permintaan listrik. Kedua sistem dianalisis dengan metoda simulasi berdasarkan aspek energi dan ekonomi menggunakan perangkat lunak PVsyst dan Homer. Dari hasil simulasi sistem PLTS 40 kWp didapatkan nilai performance ratio (PR) sebesar 0,833, renewable fraction (RF) sebesar 18,73%, dan cost of energy (COE) sebesar Rp 1.251,85/kWh yang mana nilai PR dan COE telah memenuhi target bisnis: PR > 0.8 dan COE < Rp 1.467,28/kWh tetapi tidak memenuhi target RF > 35%. Sementara dari hasil simulasi sistem PLTS 10 kWp didapatkan nilai PR sebesar 0,77, RF sebesar 44,38% hingga 52,19% pada rentang depth of discharge 20% - 80%, dan COE sebesar Rp 2.103/kWh hingga Rp 6.315/kWh pada rentang DoD 20% - 80% yang mana hanya nilai RF telah memenuhi target bisnis
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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different
degrees of aggressiveness, variable prognosis and various heterogeneous
histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic
core, active and non-enhancing core. This intrinsic heterogeneity is also
portrayed in their radio-phenotype, as their sub-regions are depicted by
varying intensity profiles disseminated across multi-parametric magnetic
resonance imaging (mpMRI) scans, reflecting varying biological properties.
Their heterogeneous shape, extent, and location are some of the factors that
make these tumors difficult to resect, and in some cases inoperable. The amount
of resected tumor is a factor also considered in longitudinal scans, when
evaluating the apparent tumor for potential diagnosis of progression.
Furthermore, there is mounting evidence that accurate segmentation of the
various tumor sub-regions can offer the basis for quantitative image analysis
towards prediction of patient overall survival. This study assesses the
state-of-the-art machine learning (ML) methods used for brain tumor image
analysis in mpMRI scans, during the last seven instances of the International
Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we
focus on i) evaluating segmentations of the various glioma sub-regions in
pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue
of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO
criteria, and iii) predicting the overall survival from pre-operative mpMRI
scans of patients that underwent gross total resection. Finally, we investigate
the challenge of identifying the best ML algorithms for each of these tasks,
considering that apart from being diverse on each instance of the challenge,
the multi-institutional mpMRI BraTS dataset has also been a continuously
evolving/growing dataset
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different
degrees of aggressiveness, variable prognosis and various heterogeneous
histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic
core, active and non-enhancing core. This intrinsic heterogeneity is also
portrayed in their radio-phenotype, as their sub-regions are depicted by
varying intensity profiles disseminated across multi-parametric magnetic
resonance imaging (mpMRI) scans, reflecting varying biological properties.
Their heterogeneous shape, extent, and location are some of the factors that
make these tumors difficult to resect, and in some cases inoperable. The amount
of resected tumor is a factor also considered in longitudinal scans, when
evaluating the apparent tumor for potential diagnosis of progression.
Furthermore, there is mounting evidence that accurate segmentation of the
various tumor sub-regions can offer the basis for quantitative image analysis
towards prediction of patient overall survival. This study assesses the
state-of-the-art machine learning (ML) methods used for brain tumor image
analysis in mpMRI scans, during the last seven instances of the International
Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we
focus on i) evaluating segmentations of the various glioma sub-regions in
pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue
of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO
criteria, and iii) predicting the overall survival from pre-operative mpMRI
scans of patients that underwent gross total resection. Finally, we investigate
the challenge of identifying the best ML algorithms for each of these tasks,
considering that apart from being diverse on each instance of the challenge,
the multi-institutional mpMRI BraTS dataset has also been a continuously
evolving/growing dataset