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GenSMILES: An enhanced validity conscious representation for inverse design of molecules

KNOWLEDGE-BASED SYSTEMS 2023年 268卷

作者： Bhadwal, Arun Singh Kumar, Kamal Kumar, Neeraj Natl Inst Technol Srinagar 246174 Uttaranchal India Thapar Inst Engn & Technol Patiala 147004 Punjab India Lebanese Amer Univ Dept Elect & Comp Engn Beirut 11022801 Lebanon King Abdulaziz Univ Fac Comp & IT Jeddah 22233 Saudi Arabia Univ Petr & Energy Studies Sch Comp Sci Dehra Dun 248007 Uttaranchal India Chandigarh Univ Dept Comp Sci & Engn Gharuan Mohali 140413 Punjab India

Deep neural networks have become increasingly important in recent years for creating molecules with desirable properties. In general, SMILES strings are used to train deep neural network based models. The trained model is then used to generate the desired molecules. Unfortunately, due to syntactical and semantic flaws in the representation, the SMILES string generates a substantial number of invalid molecules. SMILES representation fails to efficiently handle rings, branch and bonds between atoms. Lack of robustness in dealing with the cited aspects results in abundance of invalid strings. To overcome this limitation, this paper proposes a SMILES like representation, called GenSMILES. GenSMILES tackles syntactical and semantic issues by relying upon derivative rules to apply constraints. This causes a generative model produces more valid SMILES strings. By substituting a single notation for the pair representation of branches and rings in SMILES with a ) and boolean AND, respectively, GenSMILES corrects the syntactical issues. The mismatching of atom's bonds is the main cause of semantic errors. GenSMILES addresses such issues by employing derivation rules during string conversion from GenSMILES to SMILES. Every SMILES string can be represented with an equivalent GenSMILES. When used for designing drug molecule, GenSMILES increases molecule's validity when compared with SMILES and DeepSMILES on two popular architectures i.e., Recurrent Neural Network and variational autoencoder. The main benefit of GenSMILES is that it can be applied directly to generative algorithms without adapting the model environment. GenSMILES is beneficial not only to generative approaches of DL but also to the approaches that use SMILES string-like representation. On most of the datasets, GenSMILES is effective in improving validity above 90% and diversity score 15. GenSMILES results in more diversity in the properties of generated molecules and allows exploration of larger portion of undisc

关键词： SMILES GenSMILES DeepSMILES Deep learning Recurrent neural network variational autoencoder

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Domain Adaptation for Synthesis of Hazy Images

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Journal of Computer and Communications 2021年第10期9卷 142-151页

作者： Haoying Sun Yutong Zheng Qing Lang School of Information Engineering Minzu University of China Beijing China

Most existing image dehazing methods based learning are less able to perform well to real hazy images. An important reason is that they are trained on synthetic hazy images whose distribution is different from real hazy images. To relieve this issue, this paper proposes a new hazy scene generation model based on domain adaptation, which uses a variational autoencoder to encode the synthetic hazy image pairs and the real hazy images into the latent space to adapt. The synthetic hazy image pairs guide the model to learn the mapping of clear images to hazy images, the real hazy images are used to adapt the synthetic hazy images’ latent space to real hazy images through generative adversarial loss, so as to make the generative hazy images’ distribution as close to the real hazy images’ distribution as possible. By comparing the results of the model with traditional physical scattering models and Adobe Lightroom CC software, the hazy images generated in this paper is more realistic. Our end-to-end domain adaptation model is also very convenient to synthesize hazy images without depth map. Using traditional method to dehaze the synthetic hazy images generated by this paper, both SSIM and PSNR have been improved, proved that the effectiveness of our method. The non-reference haze density evaluation algorithm and other quantitative evaluation also illustrate the advantages of our method in synthetic hazy images.

关键词： Haze Scene Domain Adaptation Generative Adversarial Net variational autoencoder Deep Learning

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Near-Real-Time Identification of Seismic Damage Using Unsupervised Deep Neural Network

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JOURNAL OF ENGINEERING MECHANICS 2022年第3期148卷

作者： Kim, Minkyu Song, Junho Seoul Natl Univ Dept Civil & Environm Engn Seoul 08826 South Korea

Prompt identification of structural damage is essential for effective postdisaster responses. To this end, this paper proposes a deep neural network (DNN)-based framework to identify seismic damage based on structural response data recorded during an earthquake event. The DNN in the proposed framework is constructed by variational autoencoder, which is one of the self-supervised DNNs that can construct the continuous latent space of the input data by learning probabilistic characteristics. The DNN is trained using the flexibility matrices obtained by operational modal analysis (OMA) of simulated structural responses of the target structure under the undamaged state. To consider the load-dependency of OMA results, the undamaged state of the structure is represented by the flexibility matrix, which is closest to that obtained from the measured seismic response in the latent space. The seismic damage of each member is then estimated based on the difference between the two matrices using the flexibility disassembly method. As a numerical example, the proposed method is applied to a 5-story, 5-bay steel frame structure for which structural analyses are first performed under artificial ground motions to create train and test datasets. The proposed framework is verified with the near-real-time simulation using ground motions of El Centro and Kobe earthquakes. The example demonstrates that the proposed DNN-based method can identify seismic damage accurately in near-real-time.

关键词： Seismic response Deep neural network (DNN) variational autoencoder Near-real-time Damage identification Operational modal analysis (OMA) Flexibility matrix Flexibility disassembly method

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Partial Discharge Data Matching Method for GIS Case-Based Reasoning

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ENERGIES 2019年第19期12卷 3677页

作者： Dai, Jiejie Teng, Yingbing Zhang, Zhaoqi Yu, Zhongmin Sheng, Gehao Jiang, Xiuchen State Grid Shanghai Shinan Elect Power Supply Co Xinbei Rd 268 Shanghai 201199 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Dongchuan Rd 800 Shanghai 200240 Peoples R China State Grid Shanghai Elect Power Co Yuanshen Rd 1122 Shanghai 200120 Peoples R China

With the accumulation of partial discharge (PD) detection data from substation, case-based reasoning (CBR), which computes the match degree between detected PD data and historical case data provides new ideas for the interpretation and evaluation of partial discharge data. Aiming at the problem of partial discharge data matching, this paper proposes a data matching method based on a variational autoencoder (VAE). A VAE network model for partial discharge data is constructed to extract the deep eigenvalues. Cosine distance is then used to calculate the match degree between different partial discharge data. To verify the advantages of the proposed method, a partial discharge dataset was established through a partial discharge experiment and live detections on substation site. The proposed method was compared with other feature extraction methods and matching methods including statistical features, deep belief networks (DBN), deep convolutional neural networks (CNN), Euclidean distances, and correlation coefficients. The experimental results show that the cosine distance match degree based on the VAE feature vector can effectively detect similar partial discharge data compared with other data matching methods.

关键词： partial discharge gas insulated switchgear case-based reasoning data matching variational autoencoder

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Handling information loss of graph convolutional networks in collaborative filtering

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INFORMATION SYSTEMS 2022年 109卷

作者： Xiong, Xin Li, XunKai Hu, YouPeng Wu, YiXuan Yin, Jian Nanjing Univ Sch Artificial Intelligence 163 Xianlin Ave Nanjing Jiangsu Peoples R China Shandong Univ Sch Mech Elect & Informat Engn 180 Wenhua West Rd Weihai Shandong Peoples R China Zhejiang Univ Polytech Inst 269 Shixiang Rd Hangzhou Zhejiang Peoples R China

Collaborative filtering (CF) methods based on graph convolutional network (GCN) and autoencoder (AE) achieve outstanding performance. But the GCN-based CF methods suffer from information loss problems, which are caused by information lossy initialization and using low-order Chebyshev Polynomial to fit the graph convolution kernel. And the AE-based CF methods obtain the prediction results by reconstructing the user-item interaction matrix, which does not conduct deep excavation of the behavior patterns, resulting in the limited-expression ability. To solve the above problems, we propose variational autoencoder-Enhanced Graph Convolutional Network (VE-GCN) for CF. Specifically, we use a variational autoencoder (VAE) to compress the interactive behavior patterns as the prior information of GCN to achieve sufficient learning, thus alleviating the information lossy initialization problem. And then the generalized graph Laplacian convolution kernel is proposed in GCN to handle the high-frequency information loss problem caused by Chebyshev Polynomial fitting in the GCN-based CF. To the best of our knowledge, VE-GCN is a feasible method to handle the information loss problems mentioned above in GCN-based CF for the first time. Meanwhile, the structure of GCN is optimized by removing redundant feature transformation and nonlinear activation function, and using DenseGCN to complete multi-level information interaction. Experiments on four real-world datasets show that the VE-GCN achieves state-of-the-art performance. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Collaborative filtering Graph convolutional network Recommender system variational autoencoder

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Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM

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SENSORS 2024年第10期24卷 2992页

作者： Yang, Lei Jiang, Yibo Zeng, Kang Peng, Tao Zhejiang Univ ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou 311215 Peoples R China Jiaxing Shutuo Technol Co Ltd Jiaxing 314031 Peoples R China Zhejiang Loong Airlines Co Ltd Hangzhou 311243 Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power Components & Mechatron S Hangzhou Peoples R China

Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, accurate and robust RUL prediction of rolling bearings is of tremendous research importance. Hence, a novel RUL prediction model called CNN-VAE-MBiLSTM is proposed in this paper by integrating advantages of convolutional neural network (CNN), variational autoencoder (VAE), and multiple bi-directional long short-term memory (MBiLSTM). The proposed approach includes a CNN-VAE model and a MBiLSTM model. The CNN-VAE model performs well for automatically extracting low-dimensional features from time-frequency spectrum of multi-axis signals, which simplifies the construction of features and minimizes the subjective bias of designers. Based on these features, the MBiLSTM model achieves a commendable performance in the prediction of RUL for bearings, which independently captures sequential characteristics of features in each axis and further obtains differences among multi-axis features. The performance of the proposed approach is validated through an industrial case, and the result indicates that it exhibits a higher accuracy and a better anti-noise capacity in RUL predictions than comparable methods.

关键词： remaining useful life variational autoencoder bi-directional long short-term memory

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An Efficient Prediction Method for Coronary Heart Disease Risk Based on Two Deep Neural Networks Trained on Well-Ordered Training Datasets

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IEEE ACCESS 2021年 9卷 135210-135223页

作者： Amarbayasgalan, Tsatsral Pham, Van-Huy Theera-Umpon, Nipon Piao, Yongjun Ryu, Keun Ho Chungbuk Natl Univ Sch Elect & Comp Engn Database & Bioinformat Lab Cheongju 28644 South Korea Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City 700000 Vietnam Chiang Mai Univ Biomed Engn Inst Chiang Mai 50200 Thailand Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai 50200 Thailand Nankai Univ Sch Med Tianjin 300071 Peoples R China Tianjin Cent Hosp Gynecol Obstet Tianjin Key Lab Human Dev & Reprod Regulat Tianjin 300199 Peoples R China

This study proposes an efficient prediction method for coronary heart disease risk based on two deep neural networks trained on well-ordered training datasets. Most real datasets include an irregular subset with higher variance than most data, and predictive models do not learn well from these datasets. While most existing prediction models learned from the whole or randomly sampled training datasets, our suggested method draws up training datasets by separating regular and highly biased subsets to build accurate prediction models. We use a two-step approach to prepare the training dataset: (1) divide the initial training dataset into two groups, commonly distributed and highly biased using Principal Component Analysis, (2) enrich the highly biased group by variational autoencoders. Then, two deep neural network classifiers learn from the isolated training groups separately. The well-organized training groups enable a chance to build more accurate prediction models. When predicting the risk of coronary heart disease from the given input, only one appropriate model is selected based on the reconstruction error on the Principal Component Analysis model. Dataset used in this study was collected from the Korean National Health and Nutritional Examination Survey. We have conducted two types of experiments on the dataset. The first one proved how Principal Component Analysis and variational autoencoder models of the proposed method improves the performance of a single deep neural network. The second experiment compared the proposed method with existing machine learning algorithms, including Naive Bayes, Random Forest, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Adaptive Boosting. The experimental results show that the proposed method outperformed conventional machine learning algorithms by giving the accuracy of 0.892, specificity of 0.840, precision of 0.911, recall of 0.920, f-measure of 0.915, and AUC of 0.882.

关键词： Training Heart Principal component analysis Predictive models Diseases Deep learning Support vector machines Coronary heart disease deep neural network machine learning principal component analysis reconstruction error variational autoencoder

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Performance-Based Generative Design for Parametric Modeling of Engineering Structures Using Deep Conditional Generative Models

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AUTOMATION IN CONSTRUCTION 2023年 156卷

作者： Bucher, Martin Juan Jose Kraus, Michael Anton Rust, Romana Tang, Siyu Swiss Fed Inst Technol Stefano Franscini Pl CH-8093 Zurich Switzerland

Parametric Modeling, Generative Design, and Performance-Based Design have gained increasing attention in the AEC field as a way to create a wide range of design variants while focusing on performance attributes rather than building codes. However, the relationships between design parameters and performance attributes are often very complex, resulting in a highly iterative and unguided process. In this paper, we argue that a more goal-oriented design process is enabled by an inverse formulation that starts with performance attributes instead of design parameters. A Deep Conditional Generative Design workflow is proposed that takes a set of performance attributes and partially defined design features as input and produces a complete set of design parameters as output. A model architecture based on a Conditional variational autoencoder is presented along with different approximate posteriors, and evaluated on four different case studies. Compared to Genetic Algorithms, our method proves superior when utilizing a pre-trained model.

关键词： Deep generative modeling Performance-based design Generative design variational autoencoder Deep generative design Artificial intelligence

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Multiagent Hierarchical Cognition Difference Policy for Multiagent Cooperation

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ALGORITHMS 2021年第3期14卷 98页

作者： Wang, Huimu Liu, Zhen Yi, Jianqiang Pu, Zhiqiang Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

Multiagent cooperation is one of the most attractive research fields in multiagent systems. There are many attempts made by researchers in this field to promote cooperation behavior. However, several issues still exist, such as complex interactions among different groups of agents, redundant communication contents of irrelevant agents, which prevents the learning and convergence of agent cooperation behaviors. To address the limitations above, a novel method called multiagent hierarchical cognition difference policy (MA-HCDP) is proposed in this paper. It includes a hierarchical group network (HGN), a cognition difference network (CDN), and a soft communication network (SCN). HGN is designed to distinguish different underlying information of diverse groups' observations (including friendly group, enemy group, and object group) and extract different high-dimensional state representations of different groups. CDN is designed based on a variational auto-encoder to allow each agent to choose its neighbors (communication targets) adaptively with its environment cognition difference. SCN is designed to handle the complex interactions among the agents with a soft attention mechanism. The results of simulations demonstrate the superior effectiveness of our method compared with existing methods.

关键词： multiagent system deep reinforcement learning variational autoencoder attention mechanism

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Cost-Sensitive variational Autoencoding Classifier for Imbalanced Data Classification

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ALGORITHMS 2022年第5期15卷 139-139页

作者： Liu, Fen Qian, Quan Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Univ Mat Genome Inst Shanghai 200444 Peoples R China Zhejiang Lab Hangzhou 311100 Peoples R China

Classification is among the core tasks in machine learning. Existing classification algorithms are typically based on the assumption of at least roughly balanced data classes. When performing tasks involving imbalanced data, such classifiers ignore the minority data in consideration of the overall accuracy. The performance of traditional classification algorithms based on the assumption of balanced data distribution is insufficient because the minority-class samples are often more important than others, such as positive samples, in disease diagnosis. In this study, we propose a cost-sensitive variational autoencoding classifier that combines data-level and algorithm-level methods to solve the problem of imbalanced data classification. Cost-sensitive factors are introduced to assign a high cost to the misclassification of minority data, which biases the classifier toward minority data. We also designed misclassification costs closely related to tasks by embedding domain knowledge. Experimental results show that the proposed method performed the classification of bulk amorphous materials well.

关键词： variational autoencoder imbalanced data classification cost-sensitive learning

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