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Development of a surrogate model to improve the efficiency of groundwater level fluctuation pattern-based hydrologic properties evaluation

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JOURNAL OF HYDROLOGY 2023年第1期619卷

作者： Jeong, Jiho Jeong, Jina Kyungpook Natl Univ Dept Geol Daegu South Korea

In this study, a method of aquifer hydrologic property estimation incorporating the deep learning method was developed to improve the estimation efficiency of a process-based model based on groundwater level fluctuation (GLF) patterns. As a reference study, a data-driven method suggested by Jeong et al. (2020) was considered;the uncertainty of the GLF patterns resulting from different yearly patterns of precipitation, which were considered as noise in the previous study, was effectively discarded using the newly proposed method of applying the conditional variational autoencoder (CVAE). The CVAE was used to acquire the specific GLF patterns under certain identical precipitation patterns for all the monitoring stations. The data-driven hydrologic property estimation model was developed to predict two hydrologic parameters (rho and k) of the process-based model using the generated GLF patterns from the CVAE network as the input variables. The actual GLF and precipitation data that were acquired from nationwide groundwater monitoring stations in South Korea were applied to validate the developed method. It was found that the estimated and target hydrologic properties were highly correlated (correlation coefficients [CC]: 0.9833 and 0.9589 for rho and k, respectively), which significantly improved the results when compared to the previous study (CC: 0.7207 and 0.8663 for alpha/n and k, respectively). Consequently, the developed model can contribute to a more accurate hydrologic property estimation of aquifers. Additionally, it can facilitate efficient groundwater development planning since the manual fitting of the process-based model by an expert is not required.

关键词： Hydrologic property estimation Data-driven estimation model Groundwater level fluctuation pattern Precipitation pattern conditional variational autoencoder

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Unsupervised PM_2.5 anomalies in China induced by the COVID-19 epidemic

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 795卷 148807-148807页

作者： Zhao, Yuan Wang, Li Huang, Tao Tao, Shu Liu, Junfeng Gao, Hong Luo, Jinmu Huang, Yufei Liu, Xinrui Chen, Kaijie Wang, Linfei Ma, Jianmin Lanzhou Univ Coll Earth & Environm Sci Key Lab Environm Pollut Predict & Control Lanzhou 730000 Gansu Peoples R China Peking Univ Coll Urban & Environm Sci Lab Earth Surface Proc Beijing 100871 Peoples R China Lanzhou Univ Collaborat Innovat Ctr Western Ecol Safety Lanzhou 730000 Peoples R China

To stop the spread of COVID-19 (2019 novel coronavirus), China placed lockdown on social activities across China since mid-January 2020. The government actions significantly affected emissions of atmospheric pollutants and unintentionally created a nationwide emission reduction scenario. In order to assess the impacts of COVID-19 on fine particular matter (PM2.5) levels, we developed a "conditional variational autoencoder" (CVAE) algorithm based on the deep learning to discern unsupervised PM2.5 anomalies in Chines cities during the COVID-19 epidemic. We show that the timeline of changes in number of cities with unsupervised PM2.5 anomalies is consistent with the timeline of WHO's response to COVID-19. Using unsupervised PM2.5 anomaly as a time node, we exam-ine changes in PM2.5 before and after the time node to assess the response of PM2.5 to the COVID-19 lockdown. The rate of decrease of PM2.5 around the time node in northern China is 3.5 times faster than southern China, and decreasing PM2.5 levels in southern China is 3.5 times of that in northern China. Results were also compared with anomalous PM2.5 occurring in Chinese's Spring Festival from 2017 to 2019, PM2.5 anomalies during around Chinese New Year in 2020 differ significantly from 2017 to 2019. We demonstrate that this method could be used to detect the response of air quality to sudden changes in social activities. (C) 2021 Elsevier B.V. All rights reserved.

关键词： COVID-19 PM2.5 conditional variational autoencoder Emission reduction

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Few-shot fatigue damage evaluation of aircraft structure using neural augmentation and deep transfer learning

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ENGINEERING FAILURE ANALYSIS 2023年 148卷

作者： Che, Changchang Wang, Huawei Xiong, Minglan Luo, Shici Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing Peoples R China

To solve the problems of few-shot samples, different structural degradation trends and poor damage evaluation effect in fatigue damage evaluation of aircraft structure, an intelligent eval-uation method based on neural augmentation and deep transfer learning (NA-DTL) is proposed in this paper. Firstly, the fatigue damage is divided into three risk levels according to the length of crack, and conditional variational autoencoder (CVAE) and one-dimensional convolutional neural network (1-DCNN) are constructed to form the neural augmentation model for collaborative optimization of augmentation network and classification network. Subsequently, CVAE is used to generate massive fatigue damage samples, which can provide data support for building of crack length evaluation model. In addition, model-based transfer learning method is applied for damage evaluation according to the trained 1-DCNN. The fatigue crack growth dataset of aircraft aluminum lap joint is utilized to verify the effectiveness of the proposed method. The results show that the proposed method can achieve more accurate evaluation results compared with other models.

关键词： Fatigue damage evaluation Few-shot learning conditional variational autoencoder Neural augmentation Deep transfer learning

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Enhancing choice-set generation and route choice modeling with data- and knowledge-driven approach

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 162卷

作者： Liu, Dongjie Li, Dawei Gao, Kun Song, Yuchen Zhang, Tong Southeast Univ Sch Transportat Sipailou 2 Nanjing 210096 Peoples R China Chalmers Univ Technol Dept Architecture & Civil Engn SE-41296 Gothenburg Sweden Southeast Univ Jiangsu Key Lab Urban ITS Sipailou 2Xuanwu Dist Nanjing 210096 Peoples R China Jiangsu Prov Collaborat Innovat Ctr Modern Urban T Sipailou 2 Nanjing 210096 Peoples R China

Two central and interconnected problems arise in the specification of a "complete" path-based route choice model: choice-set generation and choice from a choice set. Choice-set generation poses a significant challenge in personalization and the enumeration of the full choice set with large size. Despite the continued prevalence of classic econometric models for modeling choices within a given set, this requirement of knowledge-driven modeling necessitates explicit model structures and intricate domain knowledge, which may result in practical biases. In this study, a conditional variational autoencoder (CVAE)-based choice set generation model is developed, which approximates the probability distribution of the underlying choice set generation process conditional on individual and OD characteristics without relying on expert knowledge. In order to facilitate a friendly integration between knowledge-driven econometric and machine learning approaches, a neural-embedded route choice model (IAP-NERCM) with implicit availability/ perception (IAP) of choice alternatives is proposed to automatically capture the heterogeneity of taste parameters without assuming any a priori relationship. Results based on synthetic data show that the proposed models are capable of reproducing the pre-defined coefficients. Field data of GPS data collected in Toyota City is used to future test the proposed models compared to classical statistical models. Results indicate that IAP-NERCM exhibits the ability to recover underlying taste function and achieves the best performance in terms of goodness-of-fit, predictability, and estimation time.

关键词： Route choice modeling Choice-set generation conditional variational autoencoder Implicit availability/perception Data and model-driven choice model

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Discriminative and generative models for anatomical shape analysis on point clouds with deep neural networks

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MEDICAL IMAGE ANALYSIS 2021年 67卷 101852-101852页

作者： Gutierrez-Becker, Benjamin Sarasua, Ignacio Wachinger, Christian Ludwig Maximilians Univ Munchen Univ Hosp Dept Child & Adolescent Psychiat Psychosomat & Ps Lab Artificial Intelligence Med Imaging AI Med Munich Germany

We introduce deep neural networks for the analysis of anatomical shapes that learn a low-dimensional shape representation from the given task, instead of relying on hand-engineered representations. Our framework is modular and consists of several computing blocks that perform fundamental shape processing tasks. The networks operate on unordered point clouds and provide invariance to similarity transformations, avoiding the need to identify point correspondences between shapes. Based on the framework, we assemble a discriminative model for disease classification and age regression, as well as a generative model for the accruate reconstruction of shapes. In particular, we propose a conditional generative model, where the condition vector provides a mechanism to control the generative process. For instance, it enables to assess shape variations specific to a particular diagnosis, when passing it as side information. Next to working on single shapes, we introduce an extension for the joint analysis of multiple anatomical structures, where the simultaneous modeling of multiple structures can lead to a more compact encoding and a better understanding of disorders. We demonstrate the advantages of our framework in comprehensive experiments on real and synthetic data. The key insights are that (i) learning a shape representation specific to the given task yields higher performance than alternative shape descriptors, (ii) multi-structure analysis is both more efficient and more accurate than single-structure analysis, and (iii) point clouds generated by our model capture morphological differences associated to Alzheimer's disease, to the point that they can be used to train a discriminative model for disease classification. Our framework naturally scales to the analysis of large datasets, giving it the potential to learn characteristic variations in large populations. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Shape analysis Deep neural networks conditional variational autoencoder Neuroanatomy

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Leveraging Past Experience for Path planning of Marine Vessel: A Docking Example

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MODELING IDENTIFICATION AND CONTROL 2022年第3期43卷 101-109页

作者： Han, Peihua Li, Guoyuan Zhang, Houxiang Norwegian Univ Sci & Technol Dept Ocean Operat & Civil Engn N-6009 Alesund Norway

Path planning before maneuvering is crucial for the safe and efficient operations of marine vessels. The past successful human maneuvering experience can be leveraged to enable the safe and efficient path planning of vessels. In this paper, the previous successful maneuvering operations from ship operators are leveraged to find the optimal path. A deep conditional generative model is used to learn the distribution from those experiences. The model is then combined with the sampling-based RRT* planning algorithm to guide the search process. In this way, the theoretical guarantee of RRT* is preserved while the sampling process is more efficient. The docking operation is used as an example to validate the method. Experimental results show that the presented method not only improves the success rate and convergence speed to the optimal cost but also generalizes well to starting points beyond maneuvering experience.

关键词： path planning RRT conditional variational autoencoder learning from past experience

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Diversifying Emotional Dialogue Generation via Selective Adversarial Training

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SENSORS 2023年第13期23卷 5904-5904页

作者： Li, Bo Zhao, Huan Zhang, Zixing Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

Emotional perception and expression are very important for building intelligent conversational systems that are human-like and attractive. Although deep neural approaches have made great progress in the field of conversation generation, there is still a lot of room for research on how to guide systems in generating responses with appropriate emotions. Meanwhile, the problem of systems' tendency to generate high-frequency universal responses remains largely unsolved. To solve this problem, we propose a method to generate diverse emotional responses through selective perturbation. Our model includes a selective word perturbation module and a global emotion control module. The former is used to introduce disturbance factors into the generated responses and enhance their expression diversity. The latter maintains the coherence of the response by limiting the emotional distribution of the response and preventing excessive deviation of emotion and meaning. Experiments are designed on two datasets, and corresponding results show that our model outperforms existing baselines in terms of emotional expression and response diversity.

关键词： dialog systems emotional response generation diversity enhancement latent variables conditional variational autoencoder

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An efficient faults and attacks categorization model in IoT-based cyber physical systems using Dilated CNN and BiLSTM with multi-scale dense Attention module

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartB期96卷

作者： Laythkhaleel, Riyam Ibrahim, Abdullahi Abdu Naseri, Raghda Awad Shaban Farhan, Hameed Mutlag Altinbas Univ Elecr & Comp Engn Mahmutbey Dilmenler Caddesi 26 TR-34217 Istanbul Turkiye Tikrit Univ Dent Dept MMH4 876 Tikrit Iraq

The physical process with the digital computing channel and electronic computing is integrated by the Cyber-Physical Systems (CPS). The abnormality and failures are the two main sources that affect the performance of the CPS. In the CPS, the research on security analysis and fault diagnosis has attracted lots of interest from the researcher. However, the existing model does not find the difference between the fault and attacks, the existing approaches need adequate development for identifying the difference between the fault and attack in the Internet of Things (IoT)-CPS. In this research, a deep learning-based approach is developed to detect the attacks and faults in the IoT-CPS for enhancing the security of the network. The IoT-based data is garnered from a standard online source. After collecting the data, the extraction of the deep features is performed using the conditional variational autoencoder (CVA). The attained deep attributes are further taken for the weighted feature fusion process in which the required weights are chosen in an optimal manner using the Enhanced Egret Swarm Optimization (EESO) algorithm. The obtained weighted fused features are inputted into the Dilated Convolutional Neural Network and Bidirectional Long Short-Term Memory with Multi-Scale Dense Attention (DCNN-Bi-LSTM-MSDA). The classification outcomes are obtained from the DCNN-Bi-LSTM-MSDA module. Throughout the result analysis, the accuracy and NPV rate of the designed model is 94.16% and 99.38%. The validation of the fault and attack classification offered by the implemented deep learning-oriented failure and abnormality classification scheme in IoT-based CPS is done against several traditional models.

关键词： Internet of Things Cyber-Physical System Fault and Attack Classification conditional variational autoencoder Enhanced Egret Swarm Optimization Algorithm Dilated Convolutional Neural Network Bidirectional Long Short-Term Memory with Multi-Scale Dense Attention

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Synthetic and privacy-preserving traffic trace generation using generative AI models for training Network Intrusion Detection Systems

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2024年 229卷

作者： Aceto, Giuseppe Giampaolo, Fabio Guida, Ciro Izzo, Stefano Pescape, Antonio Piccialli, Francesco Prezioso, Edoardo Univ Naples Federico II Dept Elect Engn & Informat Technol DIETI Via Claudio 21 I-80125 Naples Italy Univ Naples Federico II Dept Math & Applicat R Caccioppoli DMA Via Cinthia I-80126 Naples Italy Univ Bergamo Via Caniana 2 I-24127 Bergamo Italy

Network Intrusion Detection Systems (NIDS) are crucial tools for protecting networked devices from cyberattacks. Recent development in the field of Artificial Intelligence (AI) has provided tremendous advantages in implementing NIDSs able to monitor network traffic and block cyberattacks in real-time. In the literature, it is widely recognized that the effective training of a NIDS requires a large quantity of labeled traffic, representative of attacks. Nonetheless, the availability of public and abundant datasets remains remarkably restricted due to the cost of gathering and labeling real traffic traces and privacy concerns for sharing them. To tackle these challenges, in this paper we present a generative AI model capable of synthesizing anonymized traffic traces from real ones, thus dealing with privacy, abundance, and representativeness. The proposal is based on a conditional variational autoencoder (CVAE) and a preprocessing procedure specifically designed for the generation of new traffic traces. To validate our solution, we conduct an extensive empirical study leveraging three recent and publicly -available datasets, containing benign and malicious traffic. The validation is carried out from both the perspectives of classification performance of a robust NIDS and the quality of synthetic data, in comparison to the utilization of real data. We compare our CVAE with two state-of-the-art AI -based traffic data generators and prove that, trained with traces emitted by our generative model, a NIDS has a limited F1score loss compared to training on real data;competing models instead struggle or fail to generate traces that are as effective for NIDS training and as statistically similar to the original. We make the synthetic datasets available in both PCAP and tabular formats, to facilitate the reproducibility of our findings and encourage further exploration in the field of generative AI for networking.

关键词： Network Intrusion Detection System User privacy Traffic trace generation Traffic malware classification Generative artificial intelligence conditional variational autoencoder

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Highly imbalanced fault diagnosis of turbine blade cracks via deep focal dynamically weighted conditional variational auto-encoder network

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ADVANCED ENGINEERING INFORMATICS 2024年第PartA期62卷

作者： Huang, Xin Zhang, Xiaodong Xiong, Yiwei Fan, Bochao Dai, Fei Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Key Lab Educ Minist Modern Design & Rotor Bearing Syst Xian 710049 Peoples R China

Turbine blade cracks pose a critical threat to the safety of aircraft in flight. However, data collected from turbine blades often exhibit substantial imbalances in real industry settings, posing challenges for effective blade crack diagnosis using intelligent models. To solve this problem, our study presents an intelligent fault diagnosis framework utilizing three-dimensional blade tip clearance (3D-BTC) signal under highly imbalanced samples, the so-called deep focal dynamically weighted conditional variational auto -encoder (DWCVAE-DFL), which can leverage the benefits of cost -sensitive learning methods and deep generative models to effectively tackles the diagnosis complexity of imbalanced class samples, thereby improving the blade crack diagnosis performance. Specifically, the novel deep focal loss (DFL) function is first designed to consider the varying sensitivity of imbalanced class samples in feature extraction and fault classification. The blade -rotor simulation rig is used for validating the effectiveness of our proposed method. Our experimental results indicate that the performance of our proposed method outperforms the latest methods when dealing with highly imbalanced samples.

关键词： Blade crack diagnosis Imbalanced class samples conditional variational autoencoder Deep focal loss Three-dimensional blade tip clearance

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