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Long-term prediction of safety parameters with uncertainty estimation in emergency situations at nuclear power plants

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第5期55卷 1630-1643页

作者： Kim, Hyojin Kim, Jonghyun Chosun Univ Dept Nucl Engn 10Chosundae 1 gil Gwangju South Korea

The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. However, these methods only predict short-term plant status (e.g., the status after a few minutes) and do not provide information regarding the uncertainty associated with the prediction. This paper proposes an algorithm that can predict the multivariate and long-term behavior of plant parameters for 2 h with 120 steps and provide the uncertainty of the prediction. The algorithm applies bidirectional long short-term memory and an attention mechanism, which enable the algorithm to predict the precise long-term trends of the parameters with high prediction accuracy. A conditional variational autoencoder was used to provide uncertainty information about the network prediction. The algorithm was trained, optimized, and validated using a compact nuclear simulator for a Westinghouse 900 MWe NPP. (c) 2023 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Bidirectional long short-term memory Attention mechanism conditional variational autoencoder Long-term prediction Uncertainty estimation

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A-CAVE: Network abnormal traffic detection algorithm based on variational autoencoder

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ICT EXPRESS 2023年第5期9卷 896-902页

作者： Dong, Shi Su, Huadong Liu, Yue Zhoukou Normal Univ Sch Comp Sci & Technol Zhoukou 466001 Peoples R China Zhoukou Normal Univ Inst Cybersecur Zhoukou 466001 Peoples R China Wuhan Text Univ Sch Comp & Artificial Intelligence Wuhan 430200 Peoples R China

With the rapid development of the Internet and the rapid popularity of the network. Increasing network traffic also leads to frequent abnormal attacks, which seriously affects user information security. Then intrusion detection is studied as an active defense technology. In order to increase the ability of network abnormal traffic detection, a network abnormal traffic detection algorithm based on variational autoencoder is proposed in this paper. In addition to normal data, the algorithm subdivides the intrusion attack types into 4 major categories, which contain 38 specific attacks, each specific attack category as an intrusion label, which will be used as the input of the decoder together with the hidden vector to obtain the classification results more accurately and efficiently and to recover the data features in the incomplete data set. Finally, the trained Deep Belief Nets (DBN) is used for further feature extraction and classification of the data. It is verified that the proposed variational autoencoder-based supervised classification scheme is simple and effective, which is able to identify new attacks well and outperforms other classification schemes in terms of data classification. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： conditional variational autoencoder Anomaly traffic Neural network Intrusion detection

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Human Motion Trajectory Prediction Using the Social Force Model for Real-Time and Low Computational Cost Applications 6th

Human Motion Trajectory Prediction Using the Social Force Mo...

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6th Iberian Robotics Conference (Robot)

作者： Gil, Oscar Sanfeliu, Alberto CSIC UPC Inst Robot & Informat Ind Barcelona Spain

ISBN: (纸本)9783031586750;9783031586767

Human motion trajectory prediction is a very important functionality for human-robot collaboration, specifically in accompanying, guiding, or approaching tasks, but also in social robotics, self-driving vehicles, or security systems. In this paper, a novel trajectory prediction model, Social Force Generative Adversarial Network (SoFGAN), is proposed. SoFGAN uses a Generative Adversarial Network (GAN) and Social Force Model (SFM) to generate different plausible people trajectories reducing collisions in a scene. Furthermore, a conditional variational autoencoder (CVAE) module is added to emphasize the destination learning. We show that our method is more accurate in making predictions in UCY or BIWI datasets than most of the current state-of-the-art models and also reduces collisions in comparison to other approaches. Through real-life experiments, we demonstrate that the model can be used in real-time without GPU's to perform good quality predictions with a low computational cost.

关键词： Human Motion Prediction Social Force Model Generative Adversarial Network conditional variational autoencoder

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On IoT intrusion detection based on data augmentation for enhancing learning on unbalanced samples

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 133卷 213-227页

作者： Zhang, Ying Liu, Qiang Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai 201306 Peoples R China

Internet of things (IoT) security is a prerequisite for the rapid development of the IoT to enhance human well-being. Machine learning-based intrusion detection systems (IDS) have good protection capabilities. However, it is difficult to identify attack information in massive amounts of data, which leads to inefficient model detection when faced with insufficient samples for certain types of attacks. In this regard, this paper fuses deep learning methods and statistical ideas to address the problem of minority samples attack detection, and proposes an intrusion detection method for the IoT based on Improved conditional variational autoencoder (ICVAE) and Borderline Synthetic Minority Oversampling Technique (BSM), called ICVAE-BSM. By introducing an auxiliary network into the conditional variational autoencoder (CVAE) to adjust the output probability distribution of the encoder, learning the posterior distribution of different classes of samples, so that the distributions of samples of the same class are concentrated, and the distributions of different classes of samples are scattered in the latent space;then based on BSM, adaptively synthesize the edge latent variables in the latent space of ICVAE, and feed the new synthetic edge latent variables to the ICVAE's decoder to generate representative new samples to balance the data set. The output of the encoder is connected to the Softmax classifier at last, and the original samples are mixed with the generated samples to fine-tune it to enhance its generalization ability for intrusion detection of minority samples. We use the NSL-KDD data set, CIC-IDS2017 data set and CSE-CIC-IDS2018 data set to simulate and evaluate the model, the experimental results show that the proposed method can more effectively improve the accuracy of IoT attack detection under the condition of unbalanced samples. (c) 2022 Elsevier B.V. All rights reserved.

关键词： IoT intrusion detection conditional variational autoencoder Borderline-SMOTE Data augmentation

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Reconstructing Spatiotemporal Data with C-VAEs 27th

Reconstructing Spatiotemporal Data with C-VAEs

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27th European Conference on Advances in Databases and Information Systems (ADBIS)

作者： Ribeirol, Tiago F. R. Silva, Fernando Costa, Rogerio Luis de C. Polytech Leiria ESTG CIIC Leiria Portugal Polytech Leiria CIIC Leiria Portugal

ISBN: (纸本)9783031429132;9783031429149

The continuous representation of spatiotemporal data commonly relies on using abstract data types, such as moving regions, to represent entities whose shape and position continuously change over time. Creating this representation from discrete snapshots of real-world entities requires using interpolation methods to compute in-between data representations and estimate the position and shape of the object of interest at arbitrary temporal points. Existing region interpolation methods often fail to generate smooth and realistic representations of a region's evolution. However, recent advancements in deep learning techniques have revealed the potential of deep models trained on discrete observations to capture spatiotemporal dependencies through implicit feature learning. In this work, we explore the capabilities of conditional variational autoencoder (C-VAE) models to generate smooth and realistic representations of the spatiotemporal evolution of moving regions. We evaluate our proposed approach on a sparsely annotated dataset on the burnt area of a forest fire. We apply compression operations to sample from the dataset and use the C-VAE model and other commonly used interpolation algorithms to generate in-between region representations. To evaluate the performance of the methods, we compare their interpolation results with manually annotated data and regions generated by a U-Net model. We also assess the quality of generated data considering temporal consistency metrics. The proposed C-VAE-based approach demonstrates competitive results in geometric similarity metrics. It also exhibits superior temporal consistency, suggesting that C-VAE models may be a viable alternative to modeling the spatiotemporal evolution of 2D moving regions.

关键词： Region Interpolation Problem Moving Regions conditional variational autoencoder Continuous Representation

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Improving Local Fidelity of LIME by CVAE 1st

Improving Local Fidelity of LIME by CVAE

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1st World Conference on Explainable Artificial Intelligence (XAI)

作者： Yasui, Daisuke Sato, Hirosh Kubo, Masao Natl Def Acad 1-10-20 Hashirimizu Yokosuka Kanagawa Japan

ISBN: (纸本)9783031440694;9783031440700

Knowing the basis of decisions is essential for using Machine Learning (ML) in various regions, such as medical diagnosis, automated driving, and organizational decision-making. The LIME algorithm is an XAI method that can be applied to many black-box models. However, there is a problem in that the local fidelity of the interpretable model decreases. The problem is due to the sampling and weighting step of LIME;using an autoencoder is an effective way to improve this. In this study, we aim to simultaneously improve the sampling and weighting of LIME in a classification task by using a conditional variational autoencoder and filtering samples by classes. Experiments were conducted to compare the local fidelity of the proposed and existing methods for neural network classifiers trained on three medical diagnostic data sets. The results show that the proposed method improves the local fidelity compared to the existing methods. In addition, we visualized the distribution of samples in the autoencoder latent space and conducted comparative experiments by attaching and detaching components of the existing and proposed methods to analyze the factors that improve fidelity.

关键词： LIME Local fidelity conditional variational autoencoder

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CVaDeS: A conditional variational Deep Survival Model for Survival Analysis 23

CVaDeS: A Conditional Variational Deep Survival Model for Su...

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23rd IEEE International Conference on Data Mining (IEEE ICDM)

作者： Luo, Jinyuan Xiao, Zikai Shi, Wen Xie, Linhai Yang, Hong Yin, Xiaoxia Zhang, Yanchun Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou Peoples R China Natl Ctr Prot Sci Beijing State Key Lab Prote Beijing Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9798350307887

Transeriptome-hased survival modeling is a critical yet complicated task in cancer treatment due to the strong association between the prognosis of a patient and the progression of tumors based on heterogeneous molecular mechanisms. An effective prognosis model needs to consider the uncertainty that naturally stems from the high molecular heterogeneity together with numerous unforeseeable factors related to prognosis, and requires a perfect match between the selected covariates and the modeling approach of the joint distribution between covariates and right-censored survival time. Unfortunately, these necessary factors have not been fully considered in existing survival models. In this paper, we propose a novel conditional variational deep survival model (CVaDeS for short) for survival analysis. CVaDeS accurately predicts the risk values of patients and effectively distinguishes high -risk and low-risk individuals. Also, the proposed model CVaDeS can be effectively combined with Cox feature selection for survival analysis. Extensive experimental results demonstrate that the combination of Cox feature selection with CVaDeS significantly improves the predictive performance of the model, surpassing baseline models.

关键词： Survival Analysis Gene Screening conditional variational autoencoder

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Generating Robotic Emotional Body Language of Targeted Valence and Arousal with conditional variational autoencoders 15

Generating Robotic Emotional Body Language of Targeted Valen...

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15th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Marmpena, Mina Garcia, Fernando Lim, Angelica Univ Plymouth Plymouth Devon England ANYbotics AG Zurich Switzerland Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781450370578

Non-verbal communication that encompasses emotional body language is a crucial aspect of social robotics applications. Deep learning models for the generation of robotic expressions of bodily affect gain more and more ground recently over the hand-coded methods. In this work, we present a conditional variational autoencoder network that generates emotional body language animations of targeted valence and arousal for a Pepper robot, and we conduct a user study to evaluate the interpretability of the generated animations.

关键词： human-robot interaction robotic animation emotional body language deep learning conditional variational autoencoder

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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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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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