Article
Computer Science, Artificial Intelligence
Jing Lei, Xueyao Wang
Summary: In this study, a deep transfer learning prior (DTLP) is introduced to overcome the limitations imposed by low-quality tomograms on electrical capacitance tomography technology. The proposed imaging model, which incorporates imaging physical mechanisms and a new regularizer, is solved in a simpler and less computationally expensive way. A new deep transfer learning method is developed and shows performance advantages over popular imaging methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
J. Lei, Q. B. Liu
Summary: This study introduces a data-dependent prior to improve the accuracy of electrical capacitance tomography technology in the process industry. A new numerical scheme and a robust sparse semisupervised extreme learning machine method are proposed to solve the challenging imaging problem, leading to improved reconstruction quality.
APPLIED MATHEMATICAL MODELLING
(2022)
Article
Engineering, Electrical & Electronic
Daniel Ospina Acero, Qussai M. Marashdeh, Fernando L. Teixeira
Summary: In this paper, we propose an efficient synthetic electrode selection strategy for Adaptive Electrical Capacitance Volume Tomography (AECVT) based on the Adaptive Relevance Vector Machine (ARVM) method. The strategy allows for obtaining synthetic electrode configurations that can significantly decrease the image reconstruction uncertainty for the spatial distribution of the permittivity in the region of interest. By using the Reduced ARVM method, good image reconstruction and low uncertainty levels can be achieved in AECVT with considerably fewer measurements.
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2022)
Article
Computer Science, Information Systems
Wieslaw Citko, Wieslaw Sienko
Summary: This article presents a solution to the linear inverse problem of image recognition and reconstruction through a machine learning model based on a spectral processor. It proposes an alternative solution to deep learning based on optimization procedures and extends the solution to complex-valued images.
Article
Food Science & Technology
Jieming Pan, Zaifeng Yang, Stephanie Hui Kit Yap, Xiangyu Zhang, Zefeng Xu, Yida Li, Yuxuan Luo, Evgeny Zamburg, En-Xiao Liu, Chen-Khong Tham, Aaron Voon-Yew Thean
Summary: Good quality packaging is crucial in ensuring food safety and preservation. However, the seal region of packages can be a weak point, leading to unintentional contamination and compromising the integrity of the seal. To address this issue, a non-destructive high-resolution inspection approach is proposed, using enhanced sensors and reconstruction techniques to effectively validate the seal quality. A supervised autoencoder reconstruction method is introduced to overcome the challenges posed by conformal sensor placement and achieve high-quality image reconstruction.
FOOD PACKAGING AND SHELF LIFE
(2022)
Article
Chemistry, Analytical
Anna Hofmann, Martin Klein, Dirk Rueter, Andreas Sauer
Summary: A deep residual neural network (ResNet) is used to reconstruct the conductivity distribution of a biomedical, voluminous body in magnetic induction tomography (MIT). The ResNet shows good results in testing and demonstrates robustness in special test cases with unknown shapes and conductivities.
Article
Computer Science, Interdisciplinary Applications
Jing Lei, Qibin Liu, Xueyao Wang
Summary: This study introduces Regularization by Denoising (RED) to improve the reconstruction quality of electrical capacitance tomography imaging. A new numerical method is developed by integrating multiple output least squares support vector machine and low-dimensional representation method to enhance performance. The method outperforms popular imaging algorithms, providing new perspectives for the development of image reconstruction paradigm.
JOURNAL OF COMPUTATIONAL SCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Ying Wang, Shijie Sun, Yu Tian, Jiangtao Sun, Lijun Xu
Summary: A fuzzy adaptive Kalman filter (FAKaF)-based method for image reconstruction in electrical capacitance tomography (ECT) was proposed, which adjusts covariance parameters to improve image quality. Simulations and experiments show that this method performs well in terms of image quality and computational cost.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Electrical & Electronic
Liying Zhu, Lu Ma, Yi Li, Yunjie Yang, Maomao Zhang
Summary: Complex-valued electrical capacitance tomography (CV-ECT) is introduced to image both permittivity and conductivity distribution based on the same sensor of ECT. The selection of excitation frequency and linearization point is crucial for CV-ECT, and an eight-electrode CV-ECT system was set up for measurements in simulations and experiments.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Electrical & Electronic
Wael Deabes, Khalid M. Jamil Khayyat
Summary: This paper proposes a novel image reconstruction method based on a deep neural network utilizing Long Short-Term Memory (LSTM) network. Through training and testing on a large simulation dataset, it is found that the proposed LSTM-IR algorithm can create ECT images faster and more accurately compared to traditional and deep learning image reconstruction algorithms.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Jing Lei, Qibin Liu, Xueyao Wang
Summary: This article introduces an imaging model that combines learned priors with measurement physics to improve the reconstruction quality in electrical capacitance tomography. By utilizing multi-fidelity deep learning and the half-quadratic optimization method, this method is able to capture spatial details of imaging objects and obtain high-quality solutions.
DIGITAL SIGNAL PROCESSING
(2022)
Review
Energy & Fuels
Rafiul K. Rasel, Shah M. Chowdhury, Qussai M. Marashdeh, Fernando L. Teixeira
Summary: This paper provides a comprehensive review of recent advances in Electrical Capacitance Volume Tomography (ECVT) for robust monitoring of multiphase flows, especially water-containing multiphase flows.
Article
Engineering, Mechanical
Gao Xinxin, Tian Zenan, Qiu Limin, Zhang Xiaobin
Summary: In this study, a hybrid model based on deep learning is proposed for image reconstruction of cryogenic fluid electrical capacitance tomography (ECT). The model utilizes a multi-head self-attention mechanism to establish the mapping between capacitance and image, and an improved U-Net-like convolutional neural network for deep feature extraction and image reconstruction. Experimental results demonstrate that the model accurately predicts phase distribution and produces clear interfaces.
FLOW MEASUREMENT AND INSTRUMENTATION
(2022)
Article
Engineering, Electrical & Electronic
Zhi Wang, Lifeng Zhang, Boxiong Zhang, Jianhai Zhao, Sicheng Wu
Summary: This paper proposes a deep-learning-based electrical capacitance tomography (ECT) image reconstruction network with features such as multilevel dense connections and soft thresholds, achieving simplicity, high imaging accuracy, and good generalization ability.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Biomedical
Ke Zhang, Rui Guo, Maokun Li, Fan Yang, Shenheng Xu, Aria Abubakar
Summary: By using a machine learning algorithm, this study addressed the ill-posed absolute image reconstruction problem in electrical impedance tomography, achieving better accuracy and anti-noise performance compared to traditional methods. The algorithm effectively integrates prior information through a specifically designed training dataset and is capable of inverting measured thoracic data accurately.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.