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31st International Conference on Nuclear Engineering (ICONE) - 24/7 Clean Nuclear Energy - Solution for Global Warming

作者： Li, Tong Cheng, Jiahao Wang, Bo Tan, Sichao Tian, Ruifeng Harbin Engn Univ Heilongjiang Prov Key Lab Nucl Power Syst & Equip Harbin Peoples R China

ISBN: (纸本)9780791888315

In the development of the international nuclear power industry, nuclear energy safety has always been an unavoidable major issue, and nuclear energy safety concerns global energy development, public safety and national security. In the process of variable operating conditions of the thermal engineering system, incorrect alarm information may appear in the main control room due to the fluctuations of the system operation, which hinders the judgment of the actual operating state of the thermal engineering system. Although some research work has been carried out in the field of thermal system condition recognition in the world, the relevant research often adopts unsupervised machine learning model or supervised neural network model, and the application of deep reinforcement learning method is rarely studied. The deep reinforcement learning model can interact in real time in the environment, and multiple neural network models inside the deep reinforcement learning model can be trained through the guidance of the designed reward function to output different result values that can better complete the task based on different data. This method often has strong dynamic programming characteristics. It has strong theoretical feasibility in dynamic adjustment of spatial coding of thermal system data. Therefore, by combining the Convolutional auto-Encode (CAE) method in the auto-encoder and the Double Deep Q-learning (DDQN) method in the deep reinforcement learning, this research constructs an intelligent model for the recognition of abnormal working conditions of thermal systems under time series. This research takes the simulation data of Qinshan Nuclear Power Plant under varying working conditions as the data sources, and compiles the spatial encoding of the time series data through the Convolutional auto-Encode (CAE) model, which is pre-trained. Take the constructed spatial coding as the state value of the Double Deep Q-learning (DDQN) model, and guide the Double Deep Q-lear

关键词： Anomaly detection Deep reinforcement learning auto-encoder Time series data

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Urdu and MNIST Datasets Digit Recognition using CNN encoder with K-means Clustering 10

Urdu and MNIST Datasets Digit Recognition using CNN Encoder ...

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10th IEEE International Conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Kumar, Vivek Nanda, Satyasai Jagannath Malaviya Natl Inst Technol Jaipur Dept Elect & Commun Engn Jaipur 302017 Rajasthan India

ISBN: (纸本)9798350385939;9798350385922

In computer vision and machine learning, handwritten digit recognition is a fundamental issue, which finds potential applications ranging from digital document processing to postal automation. In this paper, a unique convolutional neural network (CNN) encoder followed by K-means clustering strategy is proposed for effective recognition of handwritten digits. Performance of the proposed approach is demonstrated on Urdu handwritten digits dataset and MNIST (Modified National Institute of Standards and Technology database) dataset. The performance is compared with the digits recognition with Principal Component Analysis (PCA) followed by K-means;auto- encoder followed by K-means. Simulation results reveal proposed approach has superior performance with accuracy, precision and F1-score over the two comparative models.

关键词： Handwritten digit recognition Convolutional Neural Network Principal Component Analysis auto-encoder

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Semi-supervised interlayer intelligent recognition method

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EARTH SCIENCE INFORMATICS 2023年第3期16卷 2187页

作者： Jiao, Shixiang Zhao, Jun He, Yufei Zhao, Shixuan Wu, Zhenguan Zeng, Tianyi Zhang, Rui Southwest Petr Univ Sch Geosci & Technol Chengdu 610500 Sichuan Peoples R China Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu 610500 Sichuan Peoples R China China Natl Logging Corp Geol Res Inst Xian 710077 Shaanxi Peoples R China CNPC Chuanqing Drilling Engn Co Chengdu 610051 Sichuan Peoples R China PetroChina Southwest Oil & Gas Field Co Chengdu 610000 Sichuan Peoples R China

The development and distribution of interlayers in sandstones can lead to increased heterogeneity in the formation within the reservoir, which further affects the movement of fluids in the formation. Therefore, it is essential to accurately evaluate the interlayers in sandstones, as the precise evaluation of interlayers in sandstones is of great importance for identifying the distribution of underground fluid systems. The logging data of interlayers are inadequate for traditional Machine Learning training due to their low measurement proportion compared to the conventional layers. In logging data, the amount of data in interlayers is significantly smaller than that of conventional reservoirs. Traditional machine learning models are mostly based on samples with balanced distribution. By contrast, semi-supervised learning requires a small number of labeled samples for learning, and then combines a large number of unlabeled samples for modeling. To verify the feasibility of semi-supervised learning in the identification of interlayers, the Donghe sandstone section of the H oilfield was taken as an example. First, the core analysis results were used to label the logging data;then, to uncover more response information that can characterize the interlayers on the logging curve, multiple features were extracted to construct cross features. Finally, an improved model based on autoencoders-probabilistic autoencoder (PAE)-is proposed to address the issue of interlayer recognition for imbalanced samples. The PAE model can calculate the probability of belonging to a different class for unlabeled samples, and classify new samples according to the maximum probability. Experimental results show that, compared with traditional machine learning methods and ensemble learning methods, PAE achieves higher recognition accuracy and better generalization performance by updating the algorithm, and can be used as a simple and fast method for interlayer recognition. The results of the algori

关键词： Hard area Interlayer Intelligent Recognition Deep learning Semi-supervision auto-encoder

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39th Youth Academic Annual Conference of Chinese-Association-of-automation (YAC)

作者： Li, Bo Liu, Yishun Huang, Keke Li, Yuxuan Yang, Chunhua Cent South Univ Sch Automat Changsha 410083 Peoples R China

ISBN: (纸本)9798350390780;9798350379228

Fault detection always plays an important role in industrial dynamic processes since it can avoid production loss in time. However, since industrial dynamic process becomes more and more coupled and complex, which brings uneven dynamics hidden in collected data, extracting dynamic features effectively faces lots of challenges. In addition, it is a tricky business to distinguish whether the fault that occurs is quality-related or not, resulting in unnecessary repairing and large losses. In order to deal with these issues, this paper comes up with a novel fault detection method based on quality-driven long short-term memory and autoencoder (QLSTM-AE). Specifically, LSTM network is first used to extract dynamic features, and quality variables are embedded in parallel to learn the quality-related features. Then, a fault detection strategy based on reconstruction error statistic SPE and the quality monitoring statistic H-2 is designed, which can distinguish various types of faults to realize accurate monitoring for dynamic processes. Finally, Tennessee Eastman (TE) benchmark process demonstrate the reliability and effectiveness of the proposed novel method, which indicates it has higher accuracy and can separate different faults efficiently compared to some state-of-the-art methods.

关键词： Quality-related fault detection Process monitoring auto-encoder Long Short-Term Memory

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DUNET: A ROBUST END-TO-END DEEP NEURAL NETWORK FRAMEWORK FOR IMBALANCED CLASSIFICATION 49

DUNET: A ROBUST END-TO-END DEEP NEURAL NETWORK FRAMEWORK FOR...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhang, Haotian Qi, Hong Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Imbalanced classification is a prevalent task in machine learning, characterized by unequal sample distribution among classes. The majority of classifiers aim to optimize prediction performance across all samples, leading to a bias toward the majority class. In this paper, we propose an end-to-end deep neural network framework called DuNet, specifically designed for the imbalanced classification task. In DuNet, we design a module known as the Dual auto-encoder (DAE), which adeptly generates discriminative features to differentiate between majority and minority classes. DuNet demonstrates significant superiority over other baseline models and achieves state-of-the-art results on real-world imbalanced classification datasets and the Breast Cancer Wisconsin (Diagnostic) medical dataset (WDBC).

关键词： Imbalanced classification Neural network auto-encoder KEEL repository

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New Learning Models Based on the Combination of Variational Graph autoencoder/Graph autoencoder with Context Self-supervised Learning for Link Prediction 20th

New Learning Models Based on the Combination of Variational ...

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20th International Conference on Intelligent Computing (ICIC)

作者： Zhang, Jian Gao, Yun Hai Zhang, Gui Yun Nankai Univ Tianjin Peoples R China Tianjin Normal Univ Tianjin Peoples R China

ISBN: (纸本)9789819756629;9789819756636

This study introduces two novel graph data representation learning models: Context Self-Supervised Learning Graph auto-encoders (CSSL-GAE) and Context Self-Supervised Learning Variational Graph auto-encoders (CSSLV-GAE). These models integrate graph auto-encoders (GAE), variational graph auto-encoders (VGAE), and context self-supervised learning (CSSL) to elevate the performance of link prediction tasks. Initially, we clarify the intricacies of link prediction challenges and their relevance in graph data. Following this, we offer a comprehensive introduction to the GAE and VGAE models, emphasizing their excellence in representing latent variables when learning graph structures. We then introduce CSSL, which enriches node representations by acquiring contextual information from nodes. The integration of these two models, CSSL-GAE and CSSL-VGAE, showcases remarkable performance in link prediction tasks, exhibiting stronger expressive and generalization abilities. Experimental results conducted on classic datasets, including Cora, Citeseer, and Pubmed, reveal significant performance improvements in comparison to models solely relying on GAE, VGAE, or CSSL. Ultimately, we summarize the potential influence of these models in the realm of graph data representation learning, paving the way for future research directions.

关键词： Graph Neural Networks auto-encoder Contextual Information Link Prediction

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LEARNED IMAGE COMPRESSION USING A LONG AND SHORT ATTENTION MODULE 31

LEARNED IMAGE COMPRESSION USING A LONG AND SHORT ATTENTION M...

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2024 International Conference on Image Processing

作者： Duan, Zenghui Jung, Cheolkon Liu, Yang Li, Ming Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Guangdong OPPO Mobile Telecommun Corp Dongguan Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Latent representation based on hyper-prior auto-encoders is recently applied to end-to-end image compression that shows comparable performance to the latest Versatile Video Coding (VVC) intra coding. The rate-distortion efficiency of image compression is greatly affected by the latent representation extracted by an auto-encoder. In this paper, we propose learned image compression using a long and short attention (LSA) module. We introduce the LSA module into an auto-encoder to obtain accurate latent representation of the image. The LSA module improves ability of extracting global and local image features for the auto-encoder, thereby saving bit rate and achieving higher compression efficiency. We add two LSA modules in the encoding stage and the decoding stage to improve the encoding and decoding capabilities of the learned image compression network. Experiments on the JPEG AI dataset show that the LSA module successfully reconstructs image details and thus the proposed method achieves state-of-the-art performance in terms of multi-scale structural similarity (MS-SSIM). Moreover, the proposed method outperforms the state-of-the-art methods at a low bitrate in terms of peak signal-to-noise ratio (PSNR).

关键词： Image compression auto-encoder deep learning JPEG AI long and short attention multi-head attention

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Defense of Massive False Data Injection Attack via Sparse Attack Points Considering Uncertain Topological Changes

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1588-1598页

作者： Xiaoge Huang Zhijun Qin Ming Xie Hui Liu Liang Meng School of Electrical Engineering Guangxi UniversityNanningChina Guangxi Power Grid Co.Ltd. China Southern Grid(CSG)GuilinChina

False data injection attack(FDIA)is a typical cyber-attack aiming at falsifying measurement data for state estimation(SE),which may incur catastrophic consequences on cyber-physical system *** this paper,we develop a deep learning based methodology for detection,localization,and data recovery of FDIA on power systems in a coherent and holistic ***,the multi-modal probability distributions of both measurements and state variables in SE due to ever-changing operating points and structural/topological changes pose great challenges in detecting and localizing *** address this challenge,we first propose an enhanced attack model to launch massive FDIA on limited access ***,we train an auto-encoder(AE)with a Bayesian change verification(BCV)classifier using N-1 contingencies to detect FDIA with unseen N-k operational ***,to avoid model collapse caused by multi-modal measurement distribution,an AE-based generative adversarial network(GAN)is derived to generate a diverse candidate set of normal measurement vectors with various operational ***,we develop a pattern match algorithm to localize and recover the falsified measurements and state variables by comparing the falsified measurement vectors with the normal measurement vectors in the candidate *** studies with IEEE benchmark systems and a modified 415-bus China Southern Grid system are provided to validate the proposed *** shows that the proposed methodology achieves an average 95%accuracy for detection,over 80%accuracy for localization of FDIA,and recovers the measurement and state variables close to their true values.

关键词： False data injection attack auto-encoder generative adversarial network state estimation cyber security

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A 3D Deep CNN Network-based Data Hiding Scheme for Images 10

A 3D Deep CNN Network-based Data Hiding Scheme for Images

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10th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Telli, Mounir Othmani, Mohamed Ltifi, Hela Univ Sfax Natl Engn Sch Sfax Sfax Tunisia Univ Sfax Adv Technol Environm & Smart Cities ATES Fac Sci Sfax Sfax Tunisia Univ Gafsa Fac Sci Gafsa Gafsa Tunisia Univ Kairouan Fac Sci & Tech Sidi Bouzid Kairouan Tunisia Univ Sfax Natl Engn Sch Sfax Res Grp Intelligent Machines REGIM Sfax Tunisia

ISBN: (纸本)9798350373981;9798350373974

The fundamental concept behind image steganography is to conceal one image within another. To advance steganography, it may be wise to conceal multiple images within other multiple images. The major goal of our suggested strategy is to conceal a collection of related images while taking into account size equality. With the aid of a 3D-DeepCNN grounded autoencoder, we introduce a novel multi-image steganography approach in this study. Within four cover images, we attempt to encode and decode four secret images. The quantitative findings show that the suggested model shares the embedded hidden image information over every component of the cover image, without compromising image quality. The results of our model using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are (28.56, 0.928) for global secret input and (32.516, 0.973) for global cover input. The qualitative results were evaluated and give an effective performance in comparison to the current models.

关键词： 3D Deep CNN Steganography Image auto-encoder

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Predicting remaining execution time of business process instances via auto-encoded transition system

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INTELLIGENT DATA ANALYSIS 2022年第2期26卷 543-562页

作者： Ni, Weijian Yan, Ming Liu, Tong Zeng, Qingtian Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Shandong Peoples R China Shandong Key Lab Wisdom Mine Informat Technol Qingdao Shandong Peoples R China

As an important task in business process management, remaining time prediction for business process instances has attracted extensive attentions. However, most of the traditional remaining time prediction approaches only take into account formal process models and cannot handle large-scale event logs in an effective manner. Although machine learning and deep learning have been recently applied to the remaining time prediction task, these approaches cannot incorporate domain knowledge naturally. To overcome these weaknesses of existing studies, we propose a remaining execution time prediction approach based on a novel auto-encoded transition system, which can enhance the complementarity of process modeling and deep learning techniques. Through auto-encoding the event-level and state-level features, the proposed approach can represent process instances in a comprehensive and compact form. Furthermore, a transfer learning strategy is proposed to train the remaining time prediction model so as to avoid overfitting and improve the accuracy of prediction. We conduct extensive experiments on four real-world datasets to verify the effectiveness of the proposed approach. The results show its superiority over several state-of-the-art approaches.

关键词： Business process management event log transition system auto-encoder remaining execution time

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