We present a prototype of a decentralized power trading system based on the use of distributed ledger technology. This sort of efficient, decentralized marketplace is needed to empower prosumers and make them first-class members of a smart, decentralized power grid in order to drive further renewable energy adoption. Unlike the bulk of previous work in this field, we focus on private permissioned distributed ledgers rather than conventional blockchains. The proposed solution is entirely independent of cryptocurrency, with an explicit design capability of being adapted piecemeal without any fundamental changes to the present regulatory environment. To be economical, efficient, and scalable, our prototype is based on a lean, Corda-based private permissioned distributed ledger. It allows for instant, automatic bidding on and trading of ‘power promises’ and the robust implementation of short-term, small-scale liquid electrical power futures. We demonstrate that the prototype performs well and presents several clear advantages over existing solutions based on conventional blockchains. Therefore, the proposed approach represents a promising, robust solution to the smart grid decentralized power trading problem.
This article analyzes the sensitivity of deep learning methods for ocular fundus segmentation. We use an empirical methodology based on non-adversarial perturbed datasets. The research is motivated by mass screening and self-administered tests in which AI methods are needed and may be given images with focus issues. These substandard pictures are simulated using blurring algorithms of varying designs and kernel sizes which are subjected to a test of inter-network and inter-dataset sensitivity using Gaussian noise as a control. We test for simulated defocusing, motion blur, and Gaussian noise. The DRIVE, CHASE, and STARE datasets were used to generate 441 synthetic datasets for testing. Analysis of the resultant n = 37,888 sample has identified anomalies illuminating failure modes of state-of-the-art ocular segmentation models. Data analysis led us to conclude that accuracy is a poor measure of sensitivity to input change and that even moderate levels of blur have dramatic effects on said sensitivity. We further concluded that architectures show larger variations in sensitivity to blur which do not match either their reported or measured performance on unblurred test datasets. Our analysis has attributed at least a part of the problem to overfitting non-essential input dataset features and resolution sensitivity.
U ovom radu opisani su osnovni koncepti distribuiranih sistema, blokčejn tehnologije i programskih jezika za pisanje pametnih ugovora. Razvoj blokčejn tehnologije je podeljen na tri generacije od kojih je svaka donela nove jezike za pisanje pametnih ugovora. Jezici su zasebno analizirani i na kraju je izvršena njihova komparativna analiza u cilju određivanja poželjnih osobina programskih jezika za pisanje pametnih ugovora.
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