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XBarNet: Computationally Efficient Memristor Crossbar Model Using convolutional autoencoder

IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2022年第12期41卷 5489-5500页

作者： Zhang, Yuhang He, Guanghui Wang, Guoxing Li, Yongfu Shanghai Jiao Tong Univ Dept Micronano Elect Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China

The design and verification of memristor crossbar circuits and systems demand computationally efficient models. The conventional device-level memristor model with a circuit simulator such as simulation program with integrated circuit emphasis (SPICE) to solve a memristor crossbar is time exhaustive. Hence, we propose a neural network-based memristor crossbar modeling method, XBarNet. By transforming memristor crossbar modeling to pixel-to-pixel regression, XBarNet avoids the iterative procedure in the conventional SPICE method, accelerating the runtime significantly. Meanwhile, XBarNet models the interconnect resistance and nonlinear I - V effect of memristor crossbars, which minimizes the simulation errors. We first propose a feature extraction method to bridge a memristor crossbar circuit and a neural network. Then, the network based on the convolutional autoencoder architecture is developed and the filter pruning technique is applied onto XBarNet to reduce the runtime computational cost. The experimental result shows our proposed XBarNet achieves over 78x runtime speed up and 1.7x memory reduction with only 0.28% relative error comparing to the SPICE simulator.

关键词： convolutional autoencoder interconnect memristor crossbar modeling nonlinearity

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Learning knowledge graph embedding with a bi-directional relation encoding network and a convolutional autoencoder decoding network

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NEURAL COMPUTING & APPLICATIONS 2021年第17期33卷 11157-11173页

作者： Hu, Kairong Liu, Hai Zhan, Choujun Tang, Yong Hao, Tianyong South China Normal Univ Sch Comp Sci Guangzhou Peoples R China

Derived from knowledge bases, knowledge graphs represent knowledge expressions in graphs, which utilize nodes and edges to denote entities and relations conceptually. Knowledge graph can be described in textual triple form, consisting of head entities, tail entities and relations between entities. In order to represent elements in knowledge graphs, knowledge graph embedding techniques are proposed to map entities and relations into continuous vector spaces as numeric vectors for computational efficiency. Convolution-based knowledge graph embedding models have promising performance for knowledge graph representation learning. However, the input of those neural network-based models is frequently in handmade forms and may suffer from low efficiency in feature extraction procedure of the models. In this paper, a convolutional autoencoder is proposed for knowledge graph representation learning with entity pairs as input, aiming to obtain corresponding hidden relation representation. In addition, a bi-directional relation encoding network is utilized to represent semantic of entities in different directional relation patterns, as an encoder to output representation for initialization of the convolutional autoencoder. Experiments are conducted on standard datasets including, WN18RR, Kinship, NELL-995 and FB15k-237 as a link prediction task. Besides, input embedding matrix composed of different ingredients is designed to evaluate performances of the convolutional autoencoder. The results demonstrate that our model is effective in learning representation from entity feature interactions.

关键词： Bi-directional relation convolutional autoencoder Knowledge graph Link prediction

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Multi-Modal Non-Euclidean Brain Network Analysis With Community Detection and convolutional autoencoder

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第2期7卷 436-446页

作者： Zhu, Qi Yang, Jing Wang, Shuihua Zhang, Daoqiang Zhang, Zheng Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Univ Leicester Sch Math & Actuarial Sci Leicester LE2 4SN Leics England Harbin Inst Technol Peng Cheng Lab Shenzhen 518055 Peoples R China

Brain network analysis is one of the most effective methods for brain disease diagnosis. Existing studies have shown that exploring information from multimodal data is a valuable way to improve the effectiveness of brain network analysis. In recent years, deep learning has received more and more attention due to its powerful feature learning capabilities, and it is natural to introduce this tool into multi-modal brain networks analysis. However, it would face two challenges. One is that brain network is in non-Euclidean domain, so the convolution kernel in deep learning cannot be directly used to brain networks. The other is that most of existing multi-modal brain network analysis methods cannot extract full use of complementary information from distinct modalities. In this paper, we propose a multi-modal non-Euclidean brain network analysis method based on community detection and convolutional autoencoder, which can solve the above two problems simultaneously in one framework (M2CDCA). First, we construct the functional and structural brain network, respectively. Second, we design a multi-modal interactive community detection method that exploits structural modality to guide functional modality for detecting community structure, then readjusts the nodes distribution so that the adjusted brain network can preserve the potential community information and is more suitable for convolution kernels. Finally, we design a dual-channel autoencoder model with self-attention mechanism to capture hierarchical and highly non-linear features, then comprehensively use two modalities information for classification. We evaluate our method on an epilepsy dataset, the experimental results show that our method outperform several state-of-the-art methods.

关键词： Network analyzers Brain modeling Kernel Deep learning Functional magnetic resonance imaging Diffusion tensor imaging convolutional codes Brain network analysis community detection convolutional autoencoder structural modal functional modal

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convolutional autoencoder-based SOH estimation of lithium-ion batteries using electrochemical impedance spectroscopy

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JOURNAL OF ENERGY STORAGE 2023年 60卷

作者： Obregon, Josue Han, Yu-Ri Ho, Chang Won Mouraliraman, Devanadane Lee, Chang Woo Jung, Jae-Yoon Kyung Hee Univ Dept Ind & Management Syst Engn 1732 Deogyeong daero Yongin 17104 Gyeonggi Do South Korea Kyung Hee Univ Dept Chem Engn Integrated Engn 1732 Deogyeong daero Yongin 17104 Gyeonggi Do South Korea Kyung Hee Univ Dept Big Data Analyt 1732 Deogyeong daero Yongin 17104 Gyeonggi Do South Korea

The advancement of consumer electronics and electric vehicles requires heavy use of energy sources, particularly in the form of rechargeable batteries. Although lithium-ion batteries (LiBs) enable the use of such technologies owing to their high energy and power densities, estimating the state-of-health (SOH) of such batteries remains a challenge because of the various environmental operational conditions that affect the charging and discharging cycles of LiBs. In this study, we explore an approach that uses a convolutional autoencoder (CAE) for overcomplete feature extraction from electrochemical impedance spectroscopy (EIS) data. Subsequently, the extracted latent data representation is fed into a deep neural network (DNN) for battery capacity retention and SOH estimation. The proposed end-to-end deep learning-based architecture is called CAE-DNN. To prove the effectiveness of the proposed architecture, we conducted a series of experiments using a public dataset involving EIS spectra collected from fully charged LiBs cycled at different temperatures. The experimental results were compared with those of existing state-of-the-art methods, and with other classic machine learning methods. The results demonstrate that the proposed architecture extracts useful features in an unsupervised manner and estimates the SOH of LiBs more accurately than other baseline estimation methods.

关键词： Lithium -ion batteries Deep learning State of health Electrochemical impedance spectroscopy Charge capacity estimation convolutional autoencoder

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A Lightweight Visual Font Style Recognition With Quantized convolutional autoencoder

IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY

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IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY 2024年第1期5卷 120-130页

作者： Tonmoy, Moshiur Rahman Rakib, Abdul Fattah Rahman, Rashik Adnan, Md. Akhtaruzzaman Mridha, M. F. Huang, Jie Shin, Jungpil Adv Machine Intelligence Res Lab Dhaka 1213 Bangladesh Univ Asia Pacific Dept Comp Sci & Engn Dhaka 1205 Bangladesh Amer Int Univ Bangladesh Dept Comp Sci Dhaka 1229 Bangladesh Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu 9658580 Japan

Font style recognition plays a vital role in the field of computer vision, particularly in document and pattern analysis, and image processing. In the industry context, this recognition of font styles holds immense importance for professionals such as graphic designers, front-end developers, and UI-UX developers. In recent times, font style recognition using Computer Vision has made significant progress, especially in English. Very few works have been done for other languages as well. However, the existing models are computationally costly, time-consuming, and not diversified. In this work, we propose a state-of-the-art model to recognize Bangla fonts from images using a quantized convolutional autoencoder (Q-CAE) approach. The compressed model takes around 58 KB of memory only which makes it suitable for not only high-end but also low-end computational edge devices. We have also created a synthetic data set consisting of 10 distinct Bangla font styles and a total of 60,000 images for conducting this study as no dedicated dataset is available publicly. Experimental outcomes demonstrate that the proposed method can perform better than existing methods, gaining an overall accuracy of 99.95% without quantization and 99.85% after quantization.

关键词： Visual font recognition computer vision convolutional autoencoder quantization TinyML edge devices bangla font style

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Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network

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Artificial Intelligence in Agriculture 2021年第1期5卷 90-101页

作者： Punam Bedi Pushkar Gole Department of Computer Science University of DelhiDelhiIndia

Plants are susceptive to various diseases in their growing *** detection of diseases in plants is one of the most challenging problems in *** the diseases are not identified in the early stages,then theymay adversely affect the total yield,resulting in a decrease in the farmers'*** overcome this problem,many researchers have presented different state-of-the-art systems based on Deep Learning and Machine Learning ***,most of these systems either use millions of training parameters or have lowclassification *** paper proposes a novel hybrid model based on convolutional autoencoder(CAE)network and convolutional Neural Network(CNN)for automatic plant disease *** the best of our knowledge,a hybrid system based on CAE and CNN to detect plant diseases automatically has not been proposed in any state-ofthe-art systems present in the *** this work,the proposed hybrid model is applied to detect Bacterial Spot disease present in peach plants using their leaf images,however,it can be used for any plant disease *** experiments performed in this paper use a publicly available dataset named PlantVillage to get the leaf images of peach *** proposed system achieves 99.35%training accuracy and 98.38%testing accuracy using only 9,914 training *** proposed hybrid model requires lesser number of training parameters as compared to other approaches existing in the ***,in turn,significantly decreases the time required to train the model for automatic plant disease detection and the time required to identify the disease in plants using the trained model.

关键词： Plant disease detection convolutional autoencoder convolutional neural network Deep learning in agriculture

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ICT EXPRESS 2023年第1期9卷 69-75页

作者： Ahn, Heewoong Lee, Sunhwa Ko, Hanseok Kim, Meejoung Han, Sung Won Seok, Junhee Korea Univ Dept Elect Engn Seoul 136713 South Korea Korea Univ Dept Ind Management Engn Seoul 136713 South Korea Korea Univ Res Inst Informat & Commun Technol Seoul 136713 South Korea

A weather forecaster predicts the weather by analyzing current weather map images generated by a satellite. In this analyzing process, the accuracy of the prediction depends highly on the forecaster's experience which is needed to recollect similar weather maps from the past. In an attempt to help forecasters to obtain empirical data and analyze the current weather status, this paper proposes a convolutional autoencoder model to find weather maps from the past that are similar to a current weather map by extracting the latent features of each image. To measure the similarity between each pair of images, metrics including mean squared error and structural similarity were used and case studies for searching similar satellite images were conducted and visualized. The paper also demonstrates that searching similar weather maps can be useful guidance to all forecasters when analyzing and predicting the weather.(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： Deep learning Weather map retrieval convolutional autoencoder Unsupervised learning

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convolutional autoencoder neural network for fault diagnosis with multi-sensor data

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年

作者： Wang, Xin Cui, Lingli Yang, Na Beijing Univ Technol Key Lab Adv Mfg Technol Beijing Peoples R China Beijing Univ Technol Key Lab Adv Mfg Technol Beijing Chaoyang Dist Pingleyuan 100 Beijing 100124 Peoples R China

With machines in manufacturing industry being automated, complex and intelligent, its monitoring systems are equipped with more and more smart sensors. How to extract useful features from great volume of multi-sensor data become a great challenge to the field of fault diagnosis. To overcome such challenge, an improved convolutional autoencoder neural network (CANN) is proposed to fuse and extract effective features of the color images formed by multi-sensor data in this paper. Firstly, the vibration signals of different channels are jointly transformed into color images. Secondly, an improved CANN is constructed by introducing special convolution kernels and residual connection for multi-sensor data fusion and feature extraction. Finally, the encoder part of CANN is connected with the softmax classifier for fault diagnosis. Two datasets collected from Wind Power Test-Bed and Industrial Blower Fan System are used to fully validate the effectiveness of proposed CANN. The results show that it can effectively fuse multi-sensor data and mine the discriminative features. Furthermore, compared with some related state-of-art methods, the CANN obtains higher diagnostic accuracy, especially for less labeled data.

关键词： Multi-sensor data fusion color image convolutional autoencoder residual connection

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convolutional autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants

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ENERGIES 2023年第14期16卷 5293-5293页

作者： Park, Taeseop Song, Keunju Jeong, Jaeik Kim, Hongseok Sogang Univ Dept Elect Engn Seoul 04107 South Korea Elect & Telecommun Res Inst ETRI Energy ICT Res Sect Daejeon 34129 South Korea

Machine learning-based time-series forecasting has recently been intensively studied. Deep learning (DL), specifically deep neural networks (DNN) and long short-term memory (LSTM), are the popular approaches for this purpose. However, these methods have several problems. First, DNN needs a lot of data to avoid over-fitting. Without sufficient data, the model cannot be generalized so it may not be good for unseen data. Second, impaired data affect forecasting accuracy. In general, one trains a model assuming that normal data enters the input. However, when anomalous data enters the input, the forecasting accuracy of the model may decrease substantially, which emphasizes the importance of data integrity. This paper focuses on these two problems. In time-series forecasting, especially for photovoltaic (PV) forecasting, data from solar power plants are not sufficient. As solar panels are newly installed, a sufficiently long period of data cannot be obtained. We also find that many solar power plants may contain a substantial amount of anomalous data, e.g., 30%. In this regard, we propose a data preprocessing technique leveraging convolutional autoencoder and principal component analysis (PCA) to use insufficient data with a high rate of anomaly. We compare the performance of the PV forecasting model after applying the proposed anomaly detection in constructing a virtual power plant (VPP). Extensive experiments with 2517 PV sites in the Republic of Korea, which are used for VPP construction, confirm that the proposed technique can filter out anomaly PV sites with very high accuracy, e.g., 99%, which in turn contributes to reducing the forecasting error by 23%.

关键词： convolutional autoencoder anomaly detection PV forecasting

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An approach based on convolutional autoencoder for detecting damage location in a mechanical system

An approach based on convolutional autoencoder for detecting...

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Conference on NDE 4.0, Predictive Maintenance, Communication, and Energy Systems - The Digital Transformation of NDE

作者： Bono, F. M. Radicioni, L. Bombaci, G. Somaschini, C. Cinquemani, S. Politecn Milan Milan Italy

ISBN: (纸本)9781510660854;9781510660861

In the field of structural health monitoring, the adoption of intelligent systems able to automatically detect changes in a structure are evidently attractive. A change in the baseline configuration can be an early predictor of a structural defect that has to be monitored before it reaches critical conditions. When there is no prior knowledge on the system, deep learning models such as autoencoders could effectively detect a change and enhance the capability to determine the damage location. In this paper a deep learning approach is applied to a test rig consisting of a small building model composed by four floors connected by bending springs. Modifications of the system are simulated by changing stiffness of the spring. This algorithm is compared with traditional approach based on modal parameters by carrying out experimental tests to validate the hypothesis.

关键词： Structural Health Monitoring Fault detection Neural Networks convolutional autoencoder

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