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arXiv 2021年

作者： Ma, Lianbo Li, Nan Yu, Guo Geng, Xiaoyu Cheng, Shi Wang, Xingwei Huang, Min Jin, Yaochu College of Software Northeastern University Shenyang110819 China The Institute of Intelligent Manufacturing Nanjing Tech University Nanjing211816 China The School of Computer Science Shaanxi Normal University Xi’an710062 China The College of Computer Science Northeastern University Shenyang110819 China The College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Faculty of Technology Bielefeld University Bielefeld33619 Germany The Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

In multi-objective evolutionary neural architecture search (NAS), existing predictor-based methods commonly suffer from the rank disorder issue that a candidate high-performance architecture may have a poor ranking compared with the worse architecture in terms of the trained *** alleviate the above issue, we aim to train a Pareto-wise end-to-end ranking classifier to simplify the architecture search process by transforming the complex multi-objective NAS task into a simple classification task. To this end, a classifier-based Pareto evolution approach is proposed, where an online classifier is trained to directly predict the dominance relationship between the candidate and reference architectures. Besides, an adaptive clustering method is designed to select reference architectures for the classifier, and an α-domination assisted approach is developed to address the imbalance issue of positive and negative samples. The proposed approach is compared with a number of state-of-the-art NAS methods on widely-used test datasets, and computation results show that the proposed approach is able to alleviate the rank disorder issue and outperforms other methods. Especially, the proposed method is able to find a set of promising network architectures with different model sizes ranging from 2M to 5M under diverse objectives and constraints. © 2021, CC BY.

关键词： Network architecture

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MODELING AND FUZZY CONTROL OF PERMANENT MAGNET SYNCHRONOUS MOTORS

MODELING AND FUZZY CONTROL OF PERMANENT MAGNET SYNCHRONOUS M...

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The 10th International Conference on Power Electronics, Machines and Drives (PEMD 2020)

作者： D.-K. Jukić T. Varga T. Benšić V. J. Štil M. Barukčić Automation and Control Institute TU Wien Vienna Austria Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia

This paper deals with Permanent Magnet Synchronous Motor modeling and control. The derived nonlinear dq model is a combination of Finite Element Method simulation data with the classical dq model. The so derived model keeps the precision of the Finite Element Method simulations and also the fast execution times of the dq model. The Finite Element Method data is included in the dq model using Artificial Neural Networks. The derived model is also used for controller design. The chosen controller is a PI like Fuzzy Logic Controller, which is tuned using Genetic Algorithm optimization. The Permanent Magnet Synchronous Motor with Fuzzy Logic Controller operation is tested using Real-Time (Model-in-the-Loop) simulations.

关键词： fuzzy control finite element analysis genetic algorithms neural nets permanent magnet motors machine control synchronous motors nonlinear control systems PI control electric machine analysis computing

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CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity science 2020年第2期2卷 182-193页

作者： Zhang, Shifeng Liu, Ajian Wan, Jun Liang, Yanyan Guo, Guodong Escalera, Sergio Escalante, Hugo Jair Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Faculty of Information Technology Macau University of Science and Technology 999078 China Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Institute of Deep Learning Beijing100085 China Óptica y Electrónica Instituto Nacional de Astrofísica Puebla08007 Mexico Computer Science Department CINVESTAV-Zacatenco Mexico City07360 Mexico University of Chinese Academy of Sciences Beijing100049 China Westlake University Hangzhou310024 China

Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://***/***/face-anti-spoofing/welcome/challengecvpr2019?authuser=0. © 2019 IEEE.

关键词： Face recognition

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Nonlinear Swing down Control of the Acrobot ⁎

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IFAC-PapersOnLine 2020年第2期53卷 5813-5818页

作者： Xin Xin Maki Sakurai Shinsaku Izumi Taiga Yamasaki Jinhua She Yannian Liu Faculty of Computer Science and Systems Engineering Okayama Prefectural University 111 Kuboki Soja Okayama 719-1197 Japan School of Engineering Tokyo University of Technology Hachioji 192-0982 Japan School of Automation Southeast University 2 Sipailou Nanjing 210018 China

In this paper, we concern swing down control of the Acrobot which is a 2-link planar robot with a single actuator driving the second joint, whose control objective is to stabilize the Acrobot to the downward equilibrium point with the two links in the downward position for all initial states of the Acrobot with the exception of a set of Lebesgue measure zero. To achieve this control objective, we design a nonlinear controller by using nonnegative linear feedback of the sine function of the angle of the second joint in addition to the negative linear feedback of its angular velocity. By analyzing globally the solution of the closed-loop system consisting of the Acrobot and the presented controller and focusing on the equilibrium points of the closed-loop system and their stability, we prove that the control objective is achieved provided that some conditions on two control gains are satisfied. We design the two control gains such that the real parts of the dominant poles of the linearized model of the closed-loop system around the downward equilibrium point are minimized. We provide simulation results for two Acrobots to show the effectiveness of the presented controller.

关键词： Underactuated mechanical systems Acrobot swing down control robot control nonlinear control passivity Lyapunov stability motion analysis

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Management of services of a hyperconverged infrastructure using the coordinator 1st

Management of services of a hyperconverged infrastructure us...

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1st International Conference on computer science, engineering and Education Applications, ICCSEEA2018

作者： Rolik, Oleksandr Telenyk, Sergii Zharikov, Eduard Department of Automation and Control in Technical Systems National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine Department of Theoretical Electrical Engineering and Computer Science Faculty of Electrical and Computer Engineering Cracow University of Technology Cracow Poland

ISBN: (纸本)9783319910079

Modern data centers’ providers are gradually moving away from traditional and multi-vendor IT infrastructures to open, standardized and interchangeable solutions that are based on a software defined approach to managing data center resources. The authors analyze the architectural features, requirements, limitations, hardware and software of hyperconverged infrastructures and their advantages in comparison with traditional and converged architectures deployed in data centers. The authors propose to employ two-level coordination schema to manage compute, storage, network and virtualization subsystems of hyperconverged infrastructure along with the self-management algorithms inside these subsystems. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Digital storage

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Sensitivity analysis of neural network models: Applying methods of analysis of finite fluctuations

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2019年第4期63卷 306-311页

作者： Sysoev, Anton Ciurlia, Alessandro Sheglevatych, Roman Blyumin, Semen Department of Applied Mathematics Faculty of Automation and Computer Science Lipetsk State Technical University 30 Moskovskaya str. LipetskRU-398055 Russia Department of Information Engineering Faculty of Engineering Marche Polytechnic University IT-60131 Ancona 12 Via Brecce Bianche Italy Lipetsk Region Health Department RU-398050 Lipetsk 6 Ulitsa Zegelya str Russia

As an initial stage prior to Mathematical Modeling, the information processing should provide qualitative data preparation for the construction of consistent models of technical, economic, social systems and technological processes. The question, concerning choosing the most significant input factors affecting the function of the system, is a very actual and important. This problem could be solved with the application of methods of Sensitivity Analysis. The presented paper has the purpose to show a possible approach to this problem through the method of the Analysis of Finite Fluctuations, based on Lagrange mean value theorem, to study the sensitivity of the model under consideration. The numerical example of comparing the results obtained by Sobol sensitivity coefficients, Garson algorithm and proposed approach showed the sustainability of the introduced method. There is shown, that the proposed approach is stable in the sense of applying different input datasets. In particular, the proposed approach has been applied to the construction of a neural network model identifying any anomalies present in certain medical insurances, in order to define the most significant input factors in the anomaly's detecting, discard the others and get a slim and efficient model. © 2019 Budapest University of Technology and Economics. All rights reserved.

关键词： Sensitivity analysis

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Switched-capacitor finite impulse response rotator filter

Switched-capacitor finite impulse response rotator filter

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Paweł Pawłowski Rafał Długosz Adam Dąbrowski Division of Signal Processing and Electronic Systems Jana Pawł a II 24 Poznan University of Technology Faculty of Control Robotics and Electrical Engineering and Institute of Automation and Robotics Poznań Poland Faculty of Telecommunication Computer Science and Electrical Engineering Kaliskiego 7 UTP University of Science and Technology Bydgoszcz Poland

ISBN: (数字)9788362065394

ISBN: (纸本)9781728177465

Design of the topology and layout for an analog finite impulse response (FIR) filter realized in the CMOS technology using the switched capacitor (SC) technique, is presented. The filter is based on the so called “rotator structure” supplemented with the programmable capacitor array. This array makes the on-line adjusting of the frequency response of the filter possible in a very wide range, thus, enlarges the area of applications of this circuit, e.g., to multi-standard wireless communication, software-defined radio, adaptive systems, etc. The presented circuit realizes filters of orders up to 15 (16 filter coefficients). The maximum frequency range approaches ca. 2 MHz. The circuit dissipates ca. 8 mW from the power supply.

关键词： Finite impulse response filters Capacitors IIR filters Clocks Operational amplifiers Switches Capacitance

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Data agnostic filter gating for efficient deep networks

arXiv

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arXiv 2020年

作者： Su, Xiu You, Shan Huang, Tao Xu, Hongyan Wang, Fei Qian, Chen Zhang, Changshui Xu, Chang School of Computer Science Faculty of Engineering University of Sydney Australia SenseTime Hong Kong Department of Automation Tsinghua University China School of Precision Instrument and Opto-Electronics Engineering Tianjin University China China China

To deploy a well-trained CNN model on low-end computation edge devices, it is usually supposed to compress or prune the model under certain computation budget (e.g., FLOPs). Current filter pruning methods mainly leverage feature maps to generate important scores for filters and prune those with smaller scores, which ignores the variance of input batches to the difference in sparse structure over filters. In this paper, we propose a data agnostic filter pruning method that uses an auxiliary network named Dagger module to induce pruning and takes pretrained weights as input to learn the importance of each filter. In addition, to help prune filters with certain FLOPs constraints, we leverage an explicit FLOPs-aware regularization to directly promote pruning filters toward target FLOPs. Extensive experimental results on CIFAR-10 and ImageNet datasets indicate our superiority to other state-of-the-art filter pruning methods. For example, our 50% FLOPs ResNet-50 can achieve 76.1% Top-1 accuracy on ImageNet dataset, surpassing many other filter pruning methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Budget control

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Multi-modal remote sensory learning for multi-objects over autonomous devices

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Frontiers in bioengineering and biotechnology 2025年 13卷 1430222页

作者： Aysha Naseer Naif Almudawi Hanan Aljuaid Abdulwahab Alazeb Yahay AlQahtani Asaad Algarni Ahmad Jalal Hui Liu Department of Computer Science Air University Islamabad Pakistan. Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia. Department of Computer Science College of Computer Science and Information System Najran University Najran Saudi Arabia. Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia. Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia. Department of Computer Science and Engineering College of Informatics Korea University Seoul South Korea. Guodian Nanjing Automation Co. Ltd. Nanjing China. Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and technology Nanjing China. Cognitive Systems Lab University of Bremen Bremen Germany.

Introduction:There has been an increasing focus on object segmentation within remote sensing images in recent years due to advancements in remote sensing technology and the growing significance of these images in both military and civilian realms. In these situations, it is critical to accurately and quickly identify a wide variety of objects. In many computer vision applications, scene recognition in aerial-based remote sensing imagery presents a common issue. Method:However, several challenging elements make this work especially difficult: (i) Different objects have different pixel densities; (ii) objects are not evenly distributed in remote sensing images; (iii) objects can appear differently depending on viewing angle and lighting conditions; and (iv) there are fluctuations in the number of objects, even the same type, in remote sensing images. Using a synergistic combination of Markov Random Field (MRF) for accurate labeling and Alex Net model for robust scene recognition, this work presents a novel method for the identification of remote sensing objects. During the labeling step, the use of MRF guarantees precise spatial contextual modeling, which improves comprehension of intricate interactions between nearby aerial objects. By simultaneously using deep learning model, the incorporation of Alex Net in the following classification phase enhances the model's capacity to identify complex patterns in aerial images and adapt to a variety of object attributes. Results:Experiments show that our method performs better than others in terms of classification accuracy and generalization, indicating its efficacy analysis on benchmark datasets such as UC Merced Land Use and AID. Discussion:Several performance measures were calculated to assess the efficacy of the suggested technique, including accuracy, precision, recall, error, and F1-Score. The assessment findings show a remarkable recognition rate of around 97.90% and 98.90%, on the AID and the UC Merced Land datasets,

关键词： autonomous devices deep learning computer vision likelihood estimation multi-modal multi-objects posterior probability remote sensing scene analysis

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From 2015 to 2023: How Machine Learning Aids Natural Product Analysis

arXiv

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arXiv 2024年

作者： Shi, Suwen Huang, Ziwei Gu, Xingxin Lin, Xu Zhong, Chaoying Hang, Junjie Lin, Jianli Zhong, Claire Chenwen Zhang, Lin Li, Yu Huang, Junjie Department of Chemistry Boston University BostonMA02215 United States Department of Physics Boston University BostonMA02215 United States College of Professional Studies Northeastern University BostonMA02215 United States Department of Thoracic Surgery The First Affiliated Hospital School of Medicine Zhejiang University Zhejiang Hangzhou China Department of Electrical Engineering and Automation Guangdong Ocean University Guangdong China Cancer Hospital & Shenzhen Hospital Chinese Academy of Medical Sciences Peking Union Medical College Guangdong China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Peking University Beijing China The Jockey Club School of Public Health and Primary Care Faculty of Medicine The Chinese University of Hong Kong Hong Kong Suzhou Industrial Park Monash Research Institute of Science and Technology Suzhou China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity and volume of data generated in contemporary research endeavors. Computational methodologies represent robust tools in the field of chemistry, offering the capacity to harness potent machine-learning models to yield insightful analytical outcomes. This review delves into the spectrum of computational strategies available for natural product analysis and constructs a research framework for investigating both qualitative and quantitative chemistry problems. Our objective is to present a novel perspective on the symbiosis of machine learning and chemistry, with the potential to catalyze a transformation in the field of natural product analysis. © 2024, CC BY.

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