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Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers

arXiv

引用

arXiv 2021年

作者： Liu, Wanli Li, Chen Rahaman, Md Mamunur Jiang, Tao Sun, Hongzan Wu, Xiangchen Hu, Weiming Chen, Haoyuan Sun, Changhao Yao, Yudong Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China School of Control Engineering Chengdu University of Information Technology Chengdu610225 China Shengjing Hospital China Medical University Shenyang110001 China Suzhou Ruiguan Technology Company Ltd. Suzhou215000 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Institute of Medical Informatics University of Luebeck Luebeck Germany

Cervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many errors can occur during manual screening, a computer-aided diagnosis system based on deep learning has been developed. Deep learning methods require a fixed dimension of input images, but the dimensions of clinical medical images are inconsistent. The aspect ratios of the images suffer while resizing them directly. Clinically, the aspect ratios of cells inside cytopathological images provide important information for doctors to diagnose cancer. Therefore, it is difficult to resize directly. However, many existing studies have resized the images directly and have obtained highly robust classification results. To determine a reasonable interpretation, we have conducted a series of comparative experiments. First, the raw data of the SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets. Then, the datasets are resized to 224 × 224 pixels. Finally, 22 deep learning models are used to classify the standard and scaled datasets. The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images. This conclusion is also validated via the Herlev dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Aspect ratio

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Bayesian Neural Networks Uncertainty Quantification with Cubature Rules

Bayesian Neural Networks Uncertainty Quantification with Cub...

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International Joint Conference on Neural Networks (IJCNN)

作者： Peng Wang Nidhal C. Bouaynaya Lyudmila Mihaylova Jikai Wang Qibin Zhang Renke He Automatic Control & Systems Engineering Dept. The University of Sheffield Information and Navigation College Air Force Engineering University The 32nd Research Institute of CETC Department of Automation University of Science and Technology of China Department of Electrical and Computer Engineering Rowan University

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.

关键词： Neural networks Uncertainty Bayes methods Computational modeling Gaussian distribution Two dimensional displays Weather forecasting

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Blockchain Technology Based Decentralized Energy Trading for Multiple-Microgrid Systems 3

Blockchain Technology Based Decentralized Energy Trading for...

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3rd IEEE Conference on Energy Internet and Energy System Integration, EI2 2019

作者： Li, Ting Zhang, Wei Chen, Ning Qian, Minhui Xu, Ying School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China State Key Laboratory of Operation and Control of Renewable Energy Storage Systems China EPRI Nanjing China University of Central Florida Department of Electrical Engineering Computer Science Orlando United States

ISBN: (纸本)9781728131375

With the emerging technology for distributed generation and urge of improving quality of service of power supply for energy users, more and more Microgrids (MGs) are integrated into the distributed networks to serve the energy users. These Microgrids are gradually formulating a Multi-Microgrid System (MMGS, Multi-Microgrid System), which will play an important role for the future energy supply. Building a centralized control center not only increases the expense of investment, but also brings the issues of maintaining fairness among energy users. To address these problems, this paper proposes a peer-to-peer method for energy trading of MMGS, based on the idea of decentralized trading. An auction-based trading mechanism suitable for peer-to-peer energy trading is proposed first. For this mechanism, the MGs firstly declare their energy buying bids or selling quotations. Then, the market-clearing price is determined by using a unified weight clearing algorithm in a decentralized manner. By applying the edge technology of Blockchain, the implementation of the proposed peer-to-peer energy trading method, including the architecture, procedures, security check, etc., is also discussed. The proposed Blockchain-based energy trading platform can realize the decentralized and autonomous energy trading of MGs within an MMGS. A case study with an MMGS with 10 MG units is provided to demonstrate the effectiveness of the proposed approach. © 2019 IEEE.

关键词： Blockchain

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Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails

arXiv

引用

arXiv 2021年

作者： Zhang, Yang Yu, Xin Lu, Xiaobo Liu, Ping The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Australian Institute of Artificial Intelligence University of Technology Sydney UltimoNSW2007 Australia The School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Singapore138634 Singapore

In this paper, we study the task of hallucinating an authentic high-resolution (HR) face from an occluded thumbnail. We propose a multi-stage Progressive Upsampling and Inpainting Generative Adversarial Network, dubbed Pro-UIGAN, which exploits facial geometry priors to replenish and upsample (8×) the occluded and tiny faces (16×16 pixels). Pro-UIGAN iteratively (1) estimates facial geometry priors for low-resolution (LR) faces and (2) acquires non-occluded HR face images under the guidance of the estimated priors. Our multi-stage hallucination network upsamples and inpaints occluded LR faces via a coarse-to-fine fashion, significantly reducing undesirable artifacts and blurriness. Specifically, we design a novel cross-modal attention module for facial priors estimation, in which an input face and its landmark features are formulated as queries and keys, respectively. Such a design encourages joint feature learning across the input facial and landmark features, and deep feature correspondences will be discovered by attention. Thus, facial appearance features and facial geometry priors are learned in a mutually beneficial manner. Extensive experiments show that our Pro-UIGAN attains visually pleasing completed HR faces, thus facilitating downstream tasks, i.e., face alignment, face parsing, face recognition as well as expression classification. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Novel Line Feature Description and Matching Method for Visual-Aided Inertial Navigation System

A Novel Line Feature Description and Matching Method for Vis...

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2019 3rd International Conference on Artificial Intelligence, automation and control Technologies (AIACT 2019)

作者： Xingxing Guang Yanbin Gao Pan Liu Guangchun Li College of Automation Harbin Engineering University Department of Electrical and Computer Engineering University of Calgary Beijing Institute of Control and Electronic Technology

With the rapid development of machine vision technology, more and more attention has been paid to the visual-aided inertial navigation system. It is important that to extract and track the line features at the dynamic situation in the visual-aided inertial navigation system which is based on visual line feature information to compensate attitude errors. A novel line feature description is proposed that use the SURF points to mark the LSD lines. Then, through coarse matching and fine matching, the function of continuously tracking the one line features in different images was realized. These line feature description and tracking method are applied in the visual-aided inertial navigation system, and its effectiveness is verified by the vehicle experiment.

关键词：

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Structure Selection and LPV Model Identification of a Car Steering Dynamics⁎

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IFAC-PapersOnLine 2018年第15期51卷 1086-1091页

作者： Rödönyi, Gábor Krös, Péter Pup, Dániel Soumelidis, Alexandros Szabó, Zoltán Bokor, József Systems and Control Laboratory Computer and Automation Research Institute of Hungarian Academy of Sciences Hungary Research Center of Vehicle Industry Széchenyi István University Gyr Hungary

A Linear Parameter-Varying (LPV), discrete-time black box model of an electric power assisted steering system of a passenger car is identified from open-loop step response measurement data. The goal is to provide a nominal model for control design and analysis that is able to describe the principal characteristics of the system in the whole region of steering angle and speed range of 3 to 30 km/h. Examining a set of experimental data by using classical linear time-invariant black box modeling and validation techniques, the structure of the LPV model is determined. The parameters of the model are identified based on minimizing a quadratic error criterion by nonlinear optimization algorithms. © 2018

关键词： Automobile steering equipment Electric power system control Identification (control systems) Nonlinear programming Parameter estimation Automotive control Black box model Electric power steering Linear parameter varying models Steering systems

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A Novel Blockchain Oracle Implementation Scheme Based on Application Specific Knowledge Engines

A Novel Blockchain Oracle Implementation Scheme Based on App...

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Wang, Shuai Lu, Hao Sun, Xingkai Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266109 China University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781728128535

In blockchain ecosystems, an Oracle is a service tool which provides real-world data for smart contracts and other blockchain applications. At present, there are several Oracle implementation schemes, e.g. centralized Oracles, decentralized Oracles, and hardware Oracles. However, these schemes typically suffer from single source of data and low scalability. Application Specific Knowledge Engine (ASKE) is an integrated topic/application-centered knowledge portal that supports effective information retrieval and analysis. Inspired by ASKE, in this paper, we propose a novel Oracle implementation scheme. The proposed scheme can realize multi-source data extraction and analysis, then working prototypes are demonstrated to show the validity of the scheme. © 2019 IEEE.

关键词： Blockchain

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Reputation-based Distributed Filtering Over Sensor Networks Subject to Stochastic Nonlinearity and Network-Induced Quantization

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IEEE Transactions on Network Science and Engineering 2025年

作者： Jia, Chaoqing Wang, Zidong Hu, Jun Dong, Hongli Harbin University of Science and Technology School of Automation Harbin150080 China Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin150080 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China Northeast Petroleum University Artificial Intelligence Energy Research Institute Daqing163318 China Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Daqing163318 China

In sensor networks, due to inevitable sensor faults, malfunctions, or deliberate attacks, sensors may transmit erroneous, inaccurate, or misleading data, thereby degrading overall system performance. To address this issue, an effective approach is to assign reputation scores to sensors based on their trustworthiness, historical performance, or reliability. In this paper, the reputation-based distributed filtering (RBDF) problem is considered for a class of stochastic nonlinear systems over sensor networks with network-induced quantization. A reputation mechanism is employed to mitigate the adverse effects caused by noisy, faulty, or malicious sensors. Specifically, reputations are allocated by each sensor to the data received from its neighbors, ensuring that abnormal data are assigned smaller reputation values and may even be discarded. For the first time, a recursive RBDF algorithm is proposed, wherein an upper bound of the filtering error covariance (UBFEC) is derived by solving two matrix equations. Subsequently, the filter gain is determined by minimizing the trace of UBFEC at each step. Furthermore, a sufficient condition is presented to ensure the uniform boundedness of the filtering error dynamics. Finally, a simulation example is provided to verify the feasibility and validity of the developed RBDF algorithm. © 2013 IEEE.

关键词： Digital arithmetic

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A two-mode offspring generation selection mechanism with co-evolution for sparse large-scale multiobjective optimization

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Information Sciences 2025年 718卷

作者： Xiaodong Huang Jian Wang Gaige Wang Yong Zhang Dunwei Gong Yaochu Jin Nikhil R. Pal College of Control Science and Engineering China University of Petroleum (East China) Qingdao 266580 China College of Science China University of Petroleum (East China) Qingdao 266580 China School of Computer Science and Technology Ocean University of China Qingdao Shandong 266580 China School of Information and Control Engineering China University of Mining and Technology Xuzhou Jiangsu 221000 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao Shandong 266061 China College of Engineering Westlake University Hangzhou Zhejiang 310024 China Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

Sparse large-scale multiobjective optimization problems (LSMOPs) have a wide range of practical applications. In recent years, numerous multiobjective evolutionary algorithms (MOEAs) have been developed to address the complexities of these problems. However, many existing MOEAs designed to solve sparse LSMOPs typically rely on fixed, experience-based vectors to guide offspring generation, which often makes it challenging to determine the optimal guiding vectors for different population states, leading to premature convergence and loss of population diversity. To some extent, this leads to a subjective selection of the vector used for offspring generation. To address this issue, we propose a two-mode offspring generation selection mechanism (TOGSM) that incorporates diversified sparse knowledge into the offspring generation process. The switching between these two modes is based on a designed offspring performance indicator. We also divide the population into two subpopulations by employing techniques of Pareto dominance relationship and fitness values. In each generation, the loser subpopulation generates offspring solutions during the reproduction process, under the guidance of the winner subpopulation. Experimental results confirm that TOGSM incorporating two-mode mechanism and co-evolution strategy can generate higher quality Pareto optimal solutions with faster convergence speed than the state-of-the-art (SOTA) comparative algorithms.

关键词： Multiobjective optimization

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Geometric transformations of optical orbital angular momentum spatial modes

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Science China(Physics,Mechanics & Astronomy) 2018年第2期61卷 35-40页

作者： Rui He Xin An College of Electrical and Photoelectronic Engineering West Anhui University Reseach Center of Atmos Molecules and Optical Applications West Anhui University Institute of Quantum Control and Automation West Anhui University School of Computer and Information Hefei University of Technology

With the aid of the bosonic mode conversions in two different coordinate frames, we show that （1） the coordinate eigenstate is exactly the EPR entangled state representation, and （2） the Laguerre-Ganssian （LG） mode is exactly the wave function of the common eigenvector of the orbital angular momentum and the total photon number operator. Moreover, by using the conversion of the bosonic modes, the Wigner representation of the LG mode can be obtained directly. It provides an alternative to the method of Simon and Agarwal.

关键词： orbital angular momentum entangled state representation Laguerre-Gaussian mode

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