24 papers · ranked by Valyu relevance
Xuedong Zhang, Wenlei Sun, Ke Chen, Shijie Song
To achieve real-time monitoring and intelligent maintenance of transformers, a framework based on deep vision and digital twin has been developed. An enhanced visual detection model, DETR + X, is proposed, implementing multidimensional sample data augmentation through Swin2SR and GAN networks. This model converts…
Sumit Madan, Manuel Lentzen, Johannes Brandt, Daniel Rueckert + 2 more
'Martin Hofmann-Apitius' 'Holger Fröhlich'] Deep neural networks (DNN) have fundamentally revolutionized the artificial intelligence (AI) field. The transformer model is a type of DNN that was originally used for the natural language processing tasks and has since gained more and more attention for processing various…
Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen + 9 more
'Zhenhua Liu' 'Yehui Tang' 'An Xiao' 'Chunjing Xu' 'Yixing Xu' 'Zhaohui Yang' 'Yiman Zhang' 'Dacheng Tao'] Abstract—Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism. Thanks to its strong representation capabilities…
Lambe Mutalub Adesina, Ademola Abdulkareem, Olalekan Ogunbiyi, Oladimeji Ibrahim
'Oladimeji Ibrahim'] Transformer is the most important equipment used in power system. It's ensure network stability and reliability. Transformer fails over time due to several factors such as overload, poor insulation, cellulose deterioration, poor dielectric strength of the oil etc. However, transformer owners are…
Alen Kuriakose, Kharanshu Solanki, Meblu S. Tom
In this paper, we try to create an effective mathematical model for the well-known slayer exciter transformer circuit. We aim to analyze various aspects of the slayer-exciter circuit, by using physical and computational methods. We use a computer simulation for data collection of various parameters pertaining to the…
Shujun He, Baizhen Gao, Rushant Sabnis, Qing Sun
Much work has been done to apply machine learning and deep learning to genomics tasks, but these applications usually require extensive domain knowledge and the resulting models provide very limited interpretability. Here we present the Nucleic Transformer, a conceptually simple but effective and interpretable model…
Rayan Bajwa, Murat Kaya Yapici
On-chip transformers are considered to be the primary components in many RF wireless applications. This paper provides an in-depth review of on-chip transformers, starting with a presentation on the various equivalent circuit models to represent transformer behavior and characterize their performance. Next, a…
Alessandro Tibo, Jiazhen He, Jon Paul Janet, Eva Nittinger + 1 more
How many near-neighbors does a molecule have? This is a simple, fundamental, but unsolved question in chemistry. It is key for solving many important molecular optimization problems, for example in lead optimization in drug discovery under the similarity principle assumption. Generative models can sample virtual…
Nianying Wang, Changnan Chen, Pu Chen, Jiebin Gu + 5 more
A silicon-chip-based 3D metal solenoidal transformer is proposed and developed to achieve AC-DC conversion for integrated power supply applications. With wafer-level micro electromechanical systems (MEMS) fabrication technique to form the metal casting mold and the following micro-casting technique to rapidly (within 6…
Saeed Lotfifard
— This paper explains a unified approach for teaching the electrical model of power transformers to undergraduate students using magnetic circuits. The commonly used approach for explaining the electrical model of power transformers is a hybrid approach in which magnetic circuits are used to explain the presence of…
Kelong Mao, Peilin Zhao, Tingyang Xu, Yu Rong + 2 more
With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction is formulated as a Machine Translation (MT) task. Namely, since each molecule can be represented as a Simplified Molecular-Input Line-Entry System (SMILES) string, the…
Anup Kumar, Björn Grüning, Rolf Backofen
Galaxy is a web-based open-source platform for scientific analyses. Researchers use thousands of high-quality tools and workflows for their respective analyses. Tool recommender system predicts a collection of tools that can be used to extend an analysis. In this work, a tool recommender system is developed by training…
Y. Xiang, L. Wu, K. Velitsikakis, A. L. J. Janssen
This paper investigates the transient phenomena that occur in two special cases in the Netherlands: (A) during the energization of a power transformer via a cable feeder and (B) the energization of a power transformer together with an overhead line (OHL). In Case A a 7 km long 150 kV cable and a 150/50 kV transformer…
Alexandru Dumitrescu, Emmi Jokinen, Juho Kellosalo, Ville Paavilainen + 1 more
Signal peptides are short amino acid segments present at the N-terminus of newly synthesized proteins that facilitate protein translocation into the lumen of the endoplasmic reticulum, after which they are cleaved off. Specific regions of signal peptides influence the efficiency of protein translocation, and small…
Mohsen Mahoor, Alireza Majzoobi, Zohreh S. Hosseini, Amin Khodaei
—Transformer lifetime assessments plays a vital role in reliable operation of power systems. In this paper, leveraging sensory data, an approach in estimating transformer lifetime is presented. The winding hottest-spot temperature, which is the pivotal driver that impacts transformer aging, is measured hourly via a…
Emma Tysinger, Brajesh Rai, Anton Sinitskiy
Meaningful exploration of the chemical space of druglike molecules in drug design is a highly challenging task due to a combinatorial explosion of possible modifications of molecules. In this work, we address this problem with transformer models, a type of machine learning (ML) model, with recent demonstrated success…
Bjørn Gustavsen, Álvaro Portillo, Rodrigo Ronchi, Asgeir Mjelve
—Transformer terminal equivalents obtained via admittance measurements are suitable for simulating highfrequency transient interaction between the transformer and the network. This paper augments the terminal equivalent approach with a measurement-based voltage transfer function model which permits calculation of…
M. K. Ngwenyama, M. N. Gitau
Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as…
Thang Chu, Tuan Nguyen
Previous models have shown that learning drug features from their graph representation is more efficient than learning from their strings or numeric representations. Furthermore, integrating multi-omics data of cell lines increases the performance of drug response prediction. However, these models showed drawbacks in…
Reza Jalilzadeh Hamidi
with Applications in Hardware in-the-Loop Digital Twin Authors: ['Reza Jalilzadeh Hamidi'] Abstract—This paper proposes a recursive method for integrity verification of measured transformer currents, which is suitable for the modular development of Hardware-In-the-Loop Digital Twins (HIL DTs). The Differential…
Authors not listed
Predicting protein-ligand binding affinity from three-dimensional (3D) structural data is a central task in structure-based drug discovery, yet it remains challenging due to limited data availability, structural complexity, and the sparse nature of 3D molecular representations. In this study, we investigate the…
Ross Irwin, Spyridon Dimitriadis, Jiazhen He, Esben Bjerrum
Transformer models coupled with Simplified Molecular Line Entry System (SMILES) have recently proven to be a powerful combination for solving challenges in cheminformatics. These models, however, are often developed specifically for a single application and can be very resource-intensive to train. In this work we…
William Borrelli, Joshua Schrier
Forward and retrosynthetic organic reaction prediction are challenging applications of artificial intelligence (AI) research in chemistry. IBM’s freely available RXN for Chemistry (https://rxn.res.ibm.com) treats reaction prediction as a translation problem, by using transformer-based machine learning models trained on…
Chiaki Nakamori, Tore Eriksson
Molecular descriptors are essential tools for analyzing compounds in drug discovery, but descriptors have a drawback - it is difficult to reconstruct the original compound using only descriptor data. To overcome this drawback, we used a deep learning Transformer model to restore the molecular structure from Morgan…