The paper presents a simulation approach to photogrammetry-based three-dimensional (3D) data acquisition. Photogrammetry requires capturing of series of overlapping photos with certain properties from which 3D reconstruction is later obtained. Scanning a building or a human or jewellery requires different numbers of cameras, setup parameters, spatial orientations, etc. Without precise information on how to effectively take photos, obtaining them can be tedious work without any guarantees that it will provide sufficient 3D reconstruction quality. The proposed simulation approach aims to ease the aforementioned burdens and contributes by improving the process of photogrammetry-based 3D data acquisition. The presented simulator is tested in the context of the development of a 3D scanning system for human body scanning and avatar creation. The experiments confirm that the proposed method leads to an improved quality of 3D object reconstruction in comparison to previous practice in the field of 3D human scanning. Further, it lowers the cost and shortens the time required for the industrial process of construction of 3D scanning systems, thus confirming the value and validity of the presented approach.
Power quality disturbances (PQD) have a negative impact on power quality-sensitive equipment, often resulting in great financial losses. To prevent these losses, besides detecting a PQD on time, it is important to classify it, so that appropriate recovery procedures are employed. The majority of research employs machine learning model PQD classifiers on manually extracted features from simulated or real-world signals. This paper presents an end-to-end approach that circumvents the manual feature extraction and uses signals generated from mathematical voltage sag type formulas. We developed a configurable voltage sag generator that was used to form training and validation datasets. Based on the synthetic three-phase voltage signals, we trained several end-to-end LSTM classifiers that classify voltage sags according to ABC classification. The best-performing model achieved an accuracy of over 90% in the real-world dataset.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.