“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Theory of fuzzy integrals and its applications

This study surveys the ordered weighted averaging OWA operator literature using a citation network analysis. The main goals are the historical reconstruction of scientific development of the OWA field, the identification of the dominant direction of knowledge accumulation that emerged since the publication of the first OWA paper, and to discover the most active lines of research. The results suggest, as expected, that Yager's paperIEEE Trans. Systems Man Cybernet, 181, 183-190, 1988 is the most influential paper and the starting point of all other research using OWA. Starting from his contribution, other lines of research developed and we describe them.

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Ordered Weighted Averaging Operators 1988-2014: A Citation-Based Literature Survey

READERS of Nature may recall a vigorous controversy, conducted in its correspondence columns in 1944 and 1945, on the question whether determinism. In the book which is the subject of the present review, Prof. Born comes down definitely on the side of the indeterminists. His book is not, however, in the main concerned with philosophical ideas. Rather it is an account of the. historical development of parts of theoretical physics, explaining where strict determinism has been replaced by statistical concepts. Natural Philosophy of Cause and Chance Being the Waynflete Lectures delivered in the College of St. Mary Magdalen, Oxford, in Hilary Term 1948. By Max Born. Pp. viii + 216. (Oxford: Clarendon Press ; London: Oxford University Press, 1949.) 17s. 6d. net.

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Natural Philosophy of Cause and Chance

Separating an azeotropic mixture of toluene and ethanol via heat integration pressure swing distillation

Multi-criteria, personalized route planning using quantifier-guided ordered weighted averaging operators

An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment.

Spatio-Temporal Modeling of Dynamic Phenomena in GIS.

Short-term effects of anthropogenic/natural activities on the Tehran criteria air pollutants: Source apportionment and spatiotemporal variation

High-resolution urban image clustering has remained a challenging task. This is mainly because its performance strongly depends on the discrimination power of features. Recently, several studies focused on unsupervised learning methods by autoencoders to learn and extract more efficient features for clustering purposes. This paper proposes a Boosted Convolutional AutoEncoder (BCAE) method based on feature learning for efficient urban image clustering. The proposed method was applied to multi-sensor remote-sensing images through a multistep workflow. The optical data were first preprocessed by applying a Minimum Noise Fraction (MNF) transformation. Then, these MNF features, in addition to the normalized Digital Surface Model (nDSM) and vegetation indexes such as Normalized Difference Vegetation Index (NDVI) and Excess Green (ExG(2)), were used as the inputs of the BCAE model. Next, our proposed convolutional autoencoder was trained to automatically encode upgraded features and boost the hand-crafted features for producing more clustering-friendly ones. Then, we employed the Mini Batch K-Means algorithm to cluster deep features. Finally, the comparative feature sets were manually designed in three modes to prove the efficiency of the proposed method in extracting compelling features. Experiments on three datasets show the efficiency of BCAE for feature learning. According to the experimental results, by applying the proposed method, the ultimate features become more suitable for clustering, and spatial correlation among the pixels in the feature learning process is also considered.

https://www.mdpi.com/2072-4292/13/13/2501/pdf

Saeed Nadi

Papers

Multi-criteria, personalized route planning using quantifier-guided ordered weighted averaging operators

An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment.

Spatio-Temporal Modeling of Dynamic Phenomena in GIS.

Short-term effects of anthropogenic/natural activities on the Tehran criteria air pollutants: Source apportionment and spatiotemporal variation

An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE)