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A Deep variational autoencoder Based Inverse Method for Active Energy Consumption of Mining Plants and Ball Grinding Circuit Investigation

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY 2022年第3期9卷 729-744页

作者： Tsagaan, Munkherdene Ganbat, Batdemberel Renchin, Seddorj Khurlee, Uugantuya Ichin, Oyunjargal Mongolian Univ Sci & Technol Sch Appl Sci Baga Toiruu 34 Ulaanbaatar 14191 Mongolia Tavantolgoi Shareholding Co Tsogttsetsii Town Southgobi Provi Mongolia Erdenet Min Corp Friendship Sq 1 Bayan Undur Sum 61027 Orkhon Province Mongolia

The natural ore distribution is unique, and the mining process entirely depends on it. Thus, every mining has its way of dressing ore due to the plan of industry. Therefore, in optimal and control energy systems, the relation between ore distribution, parameters of stages, and the final output are vital to understanding the entire dressing plant. In this way, the paper purpose of developing a learning-based inverse method to understand the relationship between the ore (gathered from a few different open pits) and the final recovery rate of minerals. The variational autoencoder's exceptional property is suitable for the learning-based inverse method, and the low dimensional space in the encoding and decoding process connects to the first input ore and the final outputs regarding the daily plan. When the first input ore is determined corresponding to the planned recovery, we use the low dimensional space to express the stages' appropriate parameters. The milling stage is the most crucial stage of the plant, and for the validation propose of the method, the real experiment that investigated the learning result of the selected ball milling stage. Finally, the predictive-based control system was considered based on generating a variational autoencoder-based learning feature.

关键词： variational autoencoder First input ore Final recovery Grinding circuit Predictive control Inverse method

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Monophonic Music Generation With a Given Emotion Using Conditional variational autoencoder

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IEEE ACCESS 2021年 9卷 129088-129101页

作者： Grekow, Jacek Dimitrova-Grekow, Teodora Bialystok Tech Univ Fac Comp Sci PL-15351 Bialystok Poland

The rapid increase in the importance of human-machine interaction and the accelerating pace of life pose various challenges for the creators of digital environments. Continuous improvement of human-machine interaction requires precise modeling of the physical and emotional state of people. By implementing emotional intelligence in machines, robots are expected not only to recognize and track emotions when interacting with humans, but also to respond and behave appropriately. The machine should match its reaction to the mood of the user as precisely as possible. Music generation with a given emotion can be a good start to fulfilling such a requirement. This article presents the process of building a system generating music content of a specified emotion. As the emotion labels, four basic emotions: happy, angry, sad, relaxed, corresponding to the four quarters of Russell's model, were used. Conditional variational autoencoder using a recurrent neural network for sequence processing was used as a generative model. The obtained results in the form of the generated music examples with a specific emotion are convincing in their structure and sound. The generated examples were evaluated with two methods, in the first using metrics for comparison with the training set and in the second using expert annotation.

关键词： Music Emotion recognition Man-machine systems Databases Training Buildings Service robots Generative models music generation music emotion variational autoencoder

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Topic-word-constrained sentence generation with variational autoencoder

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PATTERN RECOGNITION LETTERS 2022年 160卷 148-154页

作者： Song, Tianbao Sun, Jingbo Liu, Xin Song, Jihua Peng, Weiming Beijing Technol & Business Univ Sch Comp Sci & Engn Beijing 100048 Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China 15th Res Inst China Elect Technol Grp Corp Beijing 100083 Peoples R China

We propose a topic-word-constrained sentence-generation model with a variational autoencoder and convolutional neural network. It can generate sentences conditioned on a given topic distribution and a certain word. Unlike the vanilla variational autoencoder that assumes a standard Gaussian prior for the latent code, our model specifies the prior for the topic latent code as multiple Gaussian distributions, where each Gaussian distribution corresponds to a topic vector parameterized by a convolutional neural topic model. For word constraints, the decoder in the variational autoencoder generates sentences back-ward and forward starting from a given word. The topic latent space is arranged by the similarity of topic vectors, and the topic latent code restricts the sentence latent code through a loss term, through which expanded semantically meaningful latent spaces can be learned and provide topic guidance while gener-ating sentences. Experimental results show that our model can generate coherent and diverse sentences related to given topics and words, while also avoiding the Kullback-Leibler divergence collapse problem. Moreover, it outperforms alternative approaches in terms of sentence reconstruction, latent space prop-erty and the quality, diversity, and topic controllability of generated sentences.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Natural language generation variational autoencoder Constrained sentence generation

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CONTROL ENGINEERING PRACTICE 2022年第0期129卷

作者： Wang, Peng Ren, Shaojun Wang, Yan Zhu, Baoyu Fan, Wei Si, Fengqi Southeast Univ Key Lab Energy Thermal Convers & Control Minist Educ Nanjing 210096 Peoples R China China Inst Atom Energy Beijing 102413 Peoples R China

Quality-related process monitoring with data-driven methods has been widely researched and applied for anomaly detection in modern thermal power plants. However, the balance between the feature decomposition capability and reconstruction capability of the model is neglected, which leads to suboptimal modeling and monitoring performance. In this paper, a novel hybrid model called information bottleneck-based variational autoencoder with supervised regression (IBVAE-SR) is proposed for nonlinear system modeling and process monitoring. The beta-VAE model is extended into a hybrid network structure with a regression task to establish the relationship between the partial latent variables and quality-related variables. Moreover, some threshold coefficients are introduced in the loss function and the balance coefficients in these loss terms are fixed, which can improve the disentanglement in learning representations while ensuring the model output accuracy. The statistical metrics corresponding to the latent variables and the output of the model are designed for process monitoring. Finally, the effectiveness of the proposed method is validated via its application on a numerical simulation and an industrial medium-speed coal mill.

关键词： Quality-related Process monitoring Thermal power plants variational autoencoder Information bottleneck

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A variational autoencoder Mixture Model for Online Behavior Recommendation

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IEEE ACCESS 2020年 8卷 132736-132747页

作者： Nguyen, Minh-Duc Cho, Yoon-Sik Sejong Univ Dept Software Convergence Seoul 05006 South Korea Sejong Univ Dept Data Sci Seoul 05006 South Korea

Online behavior recommendation is an attractive research topic related to social media mining. This topic focuses on suggesting suitable behaviors for users in online platforms, including music listening, video watching, e-commerce, to name but a few to improve the user experience, an essential factor for the success of online services. A successful online behavior recommendation system should have the ability to predict behaviors that users used to performs and also suggest behaviors that users never performed before. In this paper, we develop a mixture model that contains two components to address this problem. The first component is the user-specific preference component that represents the habits of users based on their behavior history. The second component is the latent group preference component based on variational autoencoder, a deep generative neural network. This component corresponds to the hidden interests of users and allows us to discover the unseen behavior of users. We conduct experiments on various real-world datasets with different characteristics to show the performance of our model in different situations. The result indicates that our proposed model outperforms the previous mixture models for recommendation problem.

关键词： Mixture models History Probabilistic logic Data models Neural networks Task analysis Training Online behavior recommendation mixture model variational autoencoder

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A self-supervised learning model based on variational autoencoder for limited-sample mammogram classification

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APPLIED INTELLIGENCE 2024年第4期54卷 3448-3463页

作者： Karagoz, Meryem Altin Nalbantoglu, O. Ufuk Sivas Cumhuriyet Univ Dept Comp Engn Sivas Turkiye Erciyes Univ Dept Comp Engn Kayseri Turkiye Erciyes Univ Artificial Intelligence & Big Data Applicat & Res Kayseri Turkiye Erciyes Univ Genome & Stem Cell Ctr GenKok Kayseri Turkiye

Deep learning models have found extensive application in medical imaging analysis, particularly in mammography classification. However, these models encounter challenges associated with limited annotated mammography public datasets. In recent years, self-supervised learning (SSL) has emerged as a noteworthy solution to addressing data scarcity by leveraging pretext and downstream tasks. Nevertheless, we recognize a notable scarcity of self-supervised learning models designed for the classification task in mammography. In this context, we propose a novel self-supervised learning model for limited-sample mammogram classification. Our proposed SSL model comprises two primary networks. The first is a pretext task network designed to learn discriminative features through mammogram reconstruction using a variational autoencoder (VAE). Subsequently, the downstream network, dedicated to the classification of mammograms, uses the encoded space extracted by the VAE as input through a simple convolutional neural network. The performance of the proposed model is assessed on public INbreast and MIAS datasets. Comparative analyzes are conducted for the proposed model against previous studies for the same classification task and dataset. The proposed SSL model demonstrates high performance with an AUC of 0.94 for density, 0.99 for malignant-nonmalignant classifications on INbreast, 0.97 for benign-malignant, 0.99 for density, and 0.99 for normal-benign-malignant classifications on MIAS. Additionally, the proposed model reduces computational costs with only 228 trainable parameters, 204.95K FLOPs, and a depth of 3 in mammogram classification. Overall, the proposed SSL model exhibits a robust network architecture characterized by repeatability, consistency, generalization ability, and transferability among datasets, providing less computational complexity than previous studies.

关键词： Self-supervised learning Mammography Classification variational autoencoder

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Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM variational autoencoder

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IEEE ACCESS 2022年 10卷 44278-44289页

作者： Wang, Lin Tan, Haishu Zhou, Fuqiang Zuo, Wangxia Sun, Pengfei Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing 100191 Peoples R China Foshan Univ Sch Phys & Optoelect Engn Foshan 528000 Peoples R China Ji Hua Lab Foshan 528000 Peoples R China Univ South China Sch Elect Engn Hengyang 421001 Peoples R China

With the rapid increase of video surveillance points in the market in recent years, video anomaly detection has gained extensive attention in the security field. At present, the distribution of normal and anomalous data is unbalanced in unlabeled video data. variational autoencoder (VAE), as one of the typical deep generative models, gets increasingly popular in unsupervised anomaly detection. However, this model is not good at processing time-series data, especially video data. In addition, the strong generalization ability which is over-reconstructing anomaly behavior of many autoencoder-based works leads to the missed anomaly detection. To solve these problems, in this paper, we present a double-flow convolutional long short-term memory variational autoencoder (DF-ConvLSTM-VAE) to model the probabilistic distribution of the normal video in an unsupervised learning scheme, and to reconstruct videos without anomaly objects for anomaly video detection. Experiments verify the effectiveness and competitiveness of our DF-ConvLSTM-VAE on multiple public benchmark datasets. In particular, our model achieves the state-of-the-art performance on anomalous event count.

关键词： Image reconstruction Anomaly detection Data models Feature extraction Mathematical models Unsupervised learning Task analysis autoencoder variational autoencoder LSTM ConvLSTM anomaly detection

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Semi-supervised Aspect-level Sentiment Classification Model based on variational autoencoder

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KNOWLEDGE-BASED SYSTEMS 2019年第May 1期171卷 81-92页

作者： Fu, Xianghua Wei, Yanzhi Xu, Fan Wang, Ting Lu, Yu Li, Jianqiang Huang, Joshua Zhexue Shenzhen Technol Univ Fac Arts & Sci Shenzhen Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Technol Univ Coll Creat Design Shenzhen Peoples R China

Aspect-level sentiment classification aims to predict the sentiment of a text in different aspects and it is a fine-grained sentiment analysis task. Recent work exploits an Attention-based Long Short-Term Memory Network to perform aspect-level sentiment classification. Most previous work are based on supervised learning that needs a large number of labeled samples, but the problem is that only a limited subset of data samples are labeled in practical applications. To solve this problem, we propose a novel Semi-supervised Aspect Level Sentiment Classification Model based on variational autoencoder (AL-SSVAE) for semi-supervised learning in the aspect-level sentiment classification. The AL-SSVAE model inputs a given aspect to an encoder a decoder based on a variational autoencoder (VAE), and it also has an aspect level sentiment classifier. It enables the attention mechanism to deal with different parts of a text when different aspects are taken as input as previous methods. Due to that the sentiment polarity of a word is usually sensitive to the given aspect, a single vector for a word is problematic. Therefore, we propose the aspect-specific word embedding learning from a topical word embeddings model to express a word and also append the corresponding sentiment vector into the word input vector. We compare our AL-SSVAE model with several recent aspect-level sentiment classification models on the SemEval 2016 dataset. The experimental results indicate that the proposed model is able to capture more accurate semantics and sentiment for the given aspect and obtain better performance on the task of the aspect level sentiment classification. Moreover, the AL-SSVAE model is able to learn with the semi-supervised mode in the aspect level sentiment classification, which enables it to learn efficiently using less labeled data. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Aspect-level variational autoencoder Word embeddings Semi-supervised Sentiment classification

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Conditional Temporal variational autoencoder for Action Video Prediction

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2023年第10期131卷 2699-2722页

作者： Xu, Xiaogang Wang, Yi Wang, Liwei Yu, Bei Jia, Jiaya Zhejiang Lab Hangzhou Zhejiang Peoples R China Shanghai AI Lab Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

To synthesize a realistic action sequence based on a single human image, it is crucial to model both motion patterns and diversity in the action video. This paper proposes an Action Conditional Temporal variational autoencoder (ACT-VAE) to improve motion prediction accuracy and capture movement diversity. ACT-VAE predicts pose sequences for an action clip from a single input image. It is implemented as a deep generative model that maintains temporal coherence according to the action category with a novel temporal modeling on latent space. Further, ACT-VAE is a general action sequence prediction framework. When connected with a plug-and-play Pose-to-Image network, ACT-VAE can synthesize image sequences. Extensive experiments bear out our approach can predict accurate pose and synthesize realistic image sequences, surpassing state-of-the-art approaches. Compared to existing methods, ACT-VAE improves model accuracy and preserves diversity.

关键词： variational autoencoder Action modeling Temporal coherence Adversarial learning

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VAE-TALSTM: a temporal attention and variational autoencoder-based long short-term memory framework for dam displacement prediction

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ENGINEERING WITH COMPUTERS 2022年第4期38卷 3497-3512页

作者： Shu, Xiaosong Bao, Tengfei Li, Yangtao Gong, Jian Zhang, Kang Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Nanjing 210098 Peoples R China China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Peoples R China

The safety and health monitoring of dams has attracted increasing attention. In this paper, a novel prediction model based on variational autoencoder (VAE) and temporal attention-based long short-term memory (TALSTM) network is proposed for the long-term deformation of arch dams. In the proposed model, the convolutional neural network-based VAE is applied to extracting the features of environmental data. The TALSTM is employed to construct the relationship between the dam displacement and extracted features. For verification, an arch dam is taken as an example. Through the comparison among nine baseline prediction models, the proposed model is more stable and effective than other prediction models. Furthermore, the proposed model could capture the features of environmental data accurately and provide better prediction results. Therefore, the proposed model is more suitable for engineering applications.

关键词： Health monitoring Temporal attention variational autoencoder Multi-step learning Long short-term memory networks

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