Neural Network Generative Models for Radio Frequency Data

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

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Fine-tuning of a generative neural network for designing multi-target compounds

Journal of Computer-Aided Molecular Design ◽

10.1007/s10822-021-00392-8 ◽

2021 ◽

Author(s):

Thomas Blaschke ◽

Jürgen Bajorath

Keyword(s):

Neural Network ◽

Small Molecules ◽

De Novo ◽

Pharmaceutical Research ◽

Generative Models ◽

Fine Tuning ◽

Data Sets ◽

Single Target ◽

Fine Tune ◽

Target Activity

AbstractExploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical research. We have investigated the ability of a generative neural network to create multi-target compounds. Data sets of experimentally confirmed multi-target, single-target, and consistently inactive compounds were extracted from public screening data considering positive and negative assay results. These data sets were used to fine-tune the REINVENT generative model via transfer learning to systematically recognize multi-target compounds, distinguish them from single-target or inactive compounds, and construct new multi-target compounds. During fine-tuning, the model showed a clear tendency to increasingly generate multi-target compounds and structural analogs. Our findings indicate that generative models can be adopted for de novo multi-target compound design.

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Deep learning framework for material design space exploration using active transfer learning and data augmentation

npj Computational Materials ◽

10.1038/s41524-021-00609-2 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Yongtae Kim ◽

Youngsoo Kim ◽

Charles Yang ◽

Kundo Park ◽

Grace X. Gu ◽

...

Keyword(s):

Neural Network ◽

Transfer Learning ◽

Design Space Exploration ◽

Predictive Power ◽

Design Space ◽

Data Augmentation ◽

Generative Models ◽

Training Dataset ◽

Initial Training ◽

Active Transfer

AbstractNeural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome the limitation also suffer from the weak predictive power on the unseen domain. In this study, we propose a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods. We demonstrate the potential of our framework with a grid composite optimization problem that has an astronomical number of possible design configurations. Results show that our proposed framework can provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training dataset size.

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Identification of different thrombi types by analysis of 30 MHz radio-frequency data

European Journal of Ultrasound ◽

10.1016/0929-8266(96)88303-0 ◽

1996 ◽

Vol 3 (3) ◽

pp. S9 ◽

Cited By ~ 1

Author(s):

T Spencer

Keyword(s):

Radio Frequency ◽

Frequency Data

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Deep neural networks–based damage detection using vibration signals of finite element model and real intact state: An evaluation via a lab-scale offshore jacket structure

Structural Health Monitoring ◽

10.1177/1475921720932614 ◽

2020 ◽

pp. 147592172093261 ◽

Cited By ~ 2

Author(s):

Zohreh Mousavi ◽

Sina Varahram ◽

Mir Mohammad Ettefagh ◽

Morteza H. Sadeghi ◽

Seyed Naser Razavi

Keyword(s):

Neural Network ◽

Finite Element ◽

Finite Element Model ◽

Damage Detection ◽

Deep Neural Network ◽

Element Model ◽

Frequency Data ◽

Vibration Signals ◽

Intact State ◽

The Finite Element Model

Structural health monitoring of mechanical systems is essential to avoid their catastrophic failure. In this article, an effective deep neural network is developed for extracting the damage-sensitive features from frequency data of vibration signals to damage detection of mechanical systems in the presence of the uncertainties such as modeling errors, measurement errors, and environmental noises. For this purpose, the finite element method is used to analyze a mechanical system (finite element model). Then, vibration experiments are carried out on the laboratory-scale model. Vibration signals of real intact system are used to updating the finite element model and minimizing the disparities between the natural frequencies of the finite element model and real system. Some parts of the signals that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition technique. Frequency domain decomposition method is used to extract frequency data. The proposed deep neural network is trained using frequency data of the finite element model and real intact state and then is tested using frequency data of the real system. The proposed network is designed in two stages, namely, the pre-training classification based on deep auto-encoder and Softmax layer (first stage), and the re-training classification based on backpropagation algorithm for fine tuning of the network (second stage). The proposed method is validated using a lab-scale offshore jacket structure. The results show that the proposed method can learn features from the frequency data and achieve higher accuracy than other comparative methods.

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CROMO: Enhancing Crowd Mobility Characterization through Real-time Radio Frequency Data Analytics

2018 IEEE International Smart Cities Conference (ISC2) ◽

10.1109/isc2.2018.8656977 ◽

2018 ◽

Author(s):

Abdoh Jabbari ◽

Khalid J Almalki ◽

Baek-Young Choi ◽

Sejun Song

Keyword(s):

Radio Frequency ◽

Real Time ◽

Data Analytics ◽

Frequency Data

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An optimal measurement method for spatial distribution of radio frequency identification multi-tag based on image analysis and PSO

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331218823864 ◽

2019 ◽

Vol 41 (12) ◽

pp. 3331-3339

Author(s):

Xiaolei Yu ◽

Yujun Zhou ◽

Zhenlu Liu ◽

Zhimin Zhao

Keyword(s):

Neural Network ◽

Image Analysis ◽

Radio Frequency ◽

Radio Frequency Identification ◽

Template Matching ◽

Detection System ◽

Optimization Method ◽

Frequency Identification ◽

Edge Detection Method ◽

Reading Distance

In this paper, a multi-tag optimization method based on image analysis and particle swarm optimization (PSO) neural network is proposed to verify the effect of radio frequency identification (RFID) multi-tag distribution on the performance of the system. A RFID tag detection system is proposed with two charge coupled device (CCD). This system can automatically focus on the tag according to its position, so it can obtain the image information more accurately by template matching and edge detection method. Therefore, the spatial structure of multi-tag and the corresponding reading distance can be obtained for training. Because of its excellent performance in multi-objective optimization, the PSO neural network is used to train and predict multi-tag distribution at the maximum reading distance. Compared with other neural networks, PSO is more accurate and its uptime is shorter for RFID multi-tag analysis.

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