| 1. |
- Beal, Jacob, et al.
(author)
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Robust estimation of bacterial cell count from optical density
- 2020
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In: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Journal article (peer-reviewed)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
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- 2019
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Journal article (peer-reviewed)
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| 3. |
- Liao, Wenlong, et al.
(author)
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A Review of Graph Neural Networks and Their Applications in Power Systems
- 2022
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In: Journal of Modern Power Systems and Clean Energy. - : Journal of Modern Power Systems and Clean Energy. - 2196-5625 .- 2196-5420. ; 10:2, s. 345-360
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Research review (peer-reviewed)abstract
- Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks are typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high-dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNN structures, e.g., graph convolutional networks, are summarized. Key applications in power systems such as fault scenario application, time-series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed.
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| 4. |
- Liao, Wenlong, et al.
(author)
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Scenario Generation for Cooling, Heating, and Power Loads Using Generative Moment Matching Networks
- 2022
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In: CSEE JOURNAL OF POWER AND ENERGY SYSTEMS. - : Power System Technology Press. - 2096-0042. ; 8:6, s. 1730-1740
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Journal article (peer-reviewed)abstract
- Scenario generations of cooling, heating, and power loads are of great significance for the economic operation and stability analysis of integrated energy systems. In this paper, a novel deep generative network is proposed to model cooling, heating, and power load curves based on generative moment matching networks (GMMNs) where an auto-encoder transforms high-dimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples. After training the model, the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN. Unlike the explicit density models, the proposed GMMN does not need to artificially assume the probability distribution of the load curves, which leads to stronger universality. The simulation results show that the GMMN not only fits the probability distribution of multi-class load curves very well, but also accurately captures the shape (e.g., large peaks, fast ramps, and fluctuation), frequency-domain characteristics, and temporal-spatial correlations of cooling, heating, and power loads. Furthermore, the energy consumption of generated samples closely resembles that of real samples.
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| 5. |
- Xiang, Yusen, et al.
(author)
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Ginkgolic acids inhibit SARS-CoV-2 and its variants by blocking the spike protein/ACE2 interplay
- 2023
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In: International Journal of Biological Macromolecules. - : Elsevier. - 0141-8130 .- 1879-0003. ; 226, s. 780-792
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Journal article (peer-reviewed)abstract
- Targeting the interaction between the spike protein receptor binding domain (S-RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and angiotensin-converting enzyme 2 (ACE2) is a potential therapeutic strategy for treating coronavirus disease 2019 (COVID-19). However, we still lack small-molecule drug candidates for this target due to the missing knowledge in the hot spots for the protein-protein interaction. Here, we used NanoBiT technology to identify three Ginkgolic acids from an in-house traditional Chinese medicine (TCM) library, and they interfere with the S-RBD/ACE2 interplay. Our pseudovirus assay showed that one of the compounds, Ginkgolic acid C17:1 (GA171), significantly inhibits the entry of original SARS-CoV-2 and its variants into the ACE2-overexpressed HEK293T cells. We investigated and proposed the binding sites of GA171 on S-RBD by combining molecular docking and molecular dynamics simulations. Site-directed mutagenesis and surface plasmon resonance revealed that GA171 specifically binds to the pocket near R403 and Y505, critical residues of S-RBD for S-RBD interacting with ACE2. Thus, we provide structural insights into developing new small-molecule inhibitors and vaccines against the proposed S-RBD binding site.
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| 6. |
- Ge, L., et al.
(author)
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Data Augmentation Method for Transformer Fault Based on Improved Auto-Encoder Under the Condition of Insufficient Data
- 2021
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In: Diangong Jishu Xuebao/Transactions of China Electrotechnical Society. - : China Machine Press. - 1000-6753. ; 36, s. 84-94
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Journal article (peer-reviewed)abstract
- There are few transformer faults, which makes the methods of transformer fault diagnosis based on machine learning lack of data. For this reason, a method based on improved auto-encoder (IAE) is proposed to augment transformer fault data. Firstly, to solve the problem of limited data and lack of diversity in the traditional automatic encoder, an improved strategy for generating samples for transformer faults is proposed. Secondly, considering that the traditional convolutional neural network will lose a lot of feature information in the pooling operation, the improved convolutional neural network (ICNN) is constructed as the classifier of fault diagnosis. Finally, the effectiveness and adaptability of the proposed method are verified by the actual data. The simulation results show that IAE can take into account the distribution and diversity of data at the same time, and the generated transformer fault data can improve the performance of the classifier better than the traditional augmentation methods such random over-sampling method, synthetic minority over-sampling technique, and auto-encoder. Compared with traditional classifiers, ICNN has higher fault diagnosis accuracy before and after data augmentation.
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| 7. |
- Liao, Wenlong, et al.
(author)
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An Open-Source Toolbox with Classical Classifiers for Electricity Theft Detection
- 2021
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In: 2021 IEEE 2nd China International Youth Conference on Electrical Engineering, CIYCEE 2021. - : Institute of Electrical and Electronics Engineers Inc..
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Conference paper (peer-reviewed)abstract
- Recently, there is increasing interest in detecting electricity thieves for economic benefits for power companies, and many works aim to improve the accuracy of electricity theft detection. Nevertheless, a core obstacle that currently hinders the direct comparison of classifiers for electricity theft detection is the lack of a standard and public dataset, since fraudulent power load profiles are usually difficult to collect for various reasons, including cost, cumber, and confidentiality. Therefore, this paper presents an open-source toolbox, which generates different kinds of fraudulent power load profiles from attack models, and integrates classical classifiers (e.g., support vector machine, multi-layer perceptron, convolutional neural network, long short-term memory, bidirectional long short-term memory) with different performance as baselines for the comparison with new algorithms. Users can easily generate datasets and modify parameters of classical classifiers guided by user friendly interactive interfaces. The codes, toolbox, and user manuals are available online and it is free to use and extend them.
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| 8. |
- Liao, Wenlong, et al.
(author)
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Data-driven Missing Data Imputation for Wind Farms Using Context Encoder
- 2022
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In: Journal of Modern Power Systems and Clean Energy. - : Journal of Modern Power Systems and Clean Energy. - 2196-5625 .- 2196-5420. ; 10:4, s. 964-976
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Journal article (peer-reviewed)abstract
- High-quality datasets are of paramount importance for the operation and planning of wind farms. However, the datasets collected by the supervisory control and data acquisition (SCADA) system may contain missing data due to various factors such as sensor failure and communication congestion. In this paper, a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder (CE), which consists of an encoder, a decoder, and a discriminator. Through deep convolutional neural networks, the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled explicitly. The proposed method can not only fully use the surrounding context information by the reconstructed loss, but also make filling data look real by the adversarial loss. In addition, the correlation among multiple missing attributes is taken into account by adjusting the format of input data. The simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks, large valleys, and fast ramps. Moreover, the CE shows stronger generalization ability than traditional methods such as auto-encoder, K-means, k-nearest neighbor, back propagation neural network, cubic interpolation, and conditional generative adversarial network for different missing data scales.
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| 9. |
- Liao, Wenlong, et al.
(author)
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Fault diagnosis of power transformers using graph convolutional network
- 2021
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In: CSEE Journal of Power and Energy Systems. - : Power System Technology Press. - 2096-0042. ; 7:2, s. 241-249
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Journal article (peer-reviewed)abstract
- Existing methods for transformer fault diagnosis either train a classifier to fit the relationship between dissolved gas and fault type or find some similar cases with unknown samples by calculating the similarity metrics. Their accuracy is limited, since they are hard to learn from other algorithms to improve their own performance. To improve the accuracy of transformer fault diagnosis, a novel method for transformer fault diagnosis based on graph convolutional network (GCN) is proposed. The proposed method has the advantages of two kinds of existing methods. Specifically, the adjacency matrix of GCN is utilized to fully represent the similarity metrics between unknown samples and labeled samples. Furthermore, the graph convolutional layers with strong feature extraction ability are used as a classifier to find the complex nonlinear relationship between dissolved gas and fault type. The back propagation algorithm is used to complete the training process of GCN. The simulation results show that the performance of GCN is better than that of the existing methods such as convolutional neural network, multi-layer perceptron, support vector machine, extreme gradient boosting tree, k-nearest neighbors and Siamese network in different input features and data volumes, which can effectively meet the needs of diagnostic accuracy.
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| 10. |
- Liao, W., et al.
(author)
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Reactive Power Optimization of Distribution Network Based on Graph Convolutional Network
- 2021
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In: Dianwang Jishu/Power System Technology. - : Power System Technology Press. - 1000-3673. ; 45:6, s. 2150-2160
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Journal article (peer-reviewed)abstract
- The construction of advanced metering infrastructure and the rapid development of deep learning technology make it possible to quickly find the optimal strategy for reactive power optimization by mining historical data and prior knowledge instead of relying on physical models. Therefore, a method for reactive power optimization based on graph convolutional network (GCN) is proposed. Through representing the topology information between nodes in distribution network with the adjacency matrix, the proposed algorithm can effectively mine the correlation between the node loads, mapping the complex nonlinear relationship between the power equipment status and the load data with the deep graph convolutional architecture. The simulation results show that the accuracy and robustness of the GCN are better than that of the existing data-driven methods such as the convolutional neural network, the multi-layer perceptron and the case-based reasoning. Its solution time is much shorter than the traditional heuristic algorithm, which can meet the real-time demand of the reactive power optimization in distribution networks.
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