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2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019

作者： Shaikh, Salahuddin Changan, Liu Malik, Maaz Rasheed North China Electric Power Universty School of Control Computer Engineeringt Beijing China Guilin University of Electronic Technology Dept. of Information Communication Engineering Guilin China

ISBN: (纸本)9781728144528

In recently, all the developers, programmer and software engineers, they are working specially on software component and software testing to compete the software technology in the world. For this competition, they are using different kind of sources to analysis the software reliability and importance. Nowadays Data mining is one of source, which is used in software for overcome the problem of software fault which occur during the software test and its analysis. This kind of problem leads software deformity prophecy in software. In this research paper, we are also trying to overcome the software deformity prophecy problem with the help of our proposed solution called ONER rule attribute. We have used REPOSITORY datasets models, these datasets models are defected and non-defected datasets models. Our analysis class of interest is defected models. In our research, we have analyzed the efficiency of our proposed solution methods. The experiments results showed that using of ONER with discretize, have improved the efficiency of correctly classified instances in all. Using percentage split and training datasets with ONER discretize rule attribute have improved correctly classified in all datasets models. The analysis of positive accuracy f-measure is also increased in percentage split during the use of ONER with discretize but in some datasets models, the training data and cross validation is better with use of ONER rule attribute. The area under curve (ROC) in both scenarios using ONER rule attribute and discretize with ONER rule attribute is almost same or equal with each other. © 2020 IEEE.

关键词： Data mining

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Graph attention layer evolves semantic segmentation for road pothole detection: A benchmark and algorithms

arXiv

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arXiv 2021年

作者： Fan, Rui Wang, Hengli Wang, Yuan Liu, Ming Pitas, Ioannis Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR Hong Kong Industrial R&d Center SmartMore Shenzhen518000 China School of Informatics University of Thessaloniki Thessaloniki541 24 Greece

Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based. The former approaches typically employ 2-D image analysis/ understanding or 3-D point cloud modeling and segmentation algorithms to detect (i.e., recognize and localize) road potholes from vision sensor data, e.g., RGB images and/or depth/disparity images. The latter approaches generally address road pothole detection using convolutional neural networks (CNNs) in an end-to-end manner. However, road potholes are not necessarily ubiquitous and it is challenging to prepare a large wellannotated dataset for CNN training. In this regard, while computer vision-based methods were the mainstream research trend in the past decade, machine learning-based methods were merely discussed. Recently, we published the first stereo visionbased road pothole detection dataset and a novel disparity transformation algorithm, whereby the damaged and undamaged road areas can be highly distinguished. However, there are no benchmarks currently available for state-of-the-art (SoTA) CNNs trained using either disparity images or transformed disparity images. Therefore, in this paper, we first discuss the SoTA CNNs designed for semantic segmentation and evaluate their performance for road pothole detection with extensive experiments. Additionally, inspired by graph neural network (GNN), we propose a novel CNN layer, referred to as graph attention layer (GAL), which can be easily deployed in any existing CNN to optimize image feature representations for semantic segmentation. Our experiments compare GAL-DeepLabv3+, our best-performing implementation, with nine SoTA CNNs on three modalities of training data: RGB images, disparity images, and transformed disparity images. The experimental results suggest that our proposed GAL-DeepLabv3+ achieves the best overall pothole detection accuracy on all training data modalities. The source code, dataset, and benchmark are publicly available at m

关键词： Landforms

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A biologically inspired feature enhancement framework for zero-shot learning

arXiv

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arXiv 2020年

作者： Xie, Zhongwu Cao, Weipeng Wang, Xizhao Ming, Zhong Zhang, Jingjing Zhang, Jiyong College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Command and Control Engineering Army Engineering University of People’s Liberation Army of China Nanjing China School of Automation Hangzhou Dianzi University Hangzhou China

Most of the Zero-Shot Learning (ZSL) algorithms currently use pre-trained models as their feature extractors, which are usually trained on the ImageNet data set by using deep neural networks. The richness of the feature information embedded in the pre-trained models can help the ZSL model extract more useful features from its limited training samples. However, sometimes the difference between the training data set of the current ZSL task and the ImageNet data set is too large, which may lead to the use of pre-trained models has no obvious help or even negative impact on the performance of the ZSL model. To solve this problem, this paper proposes a biologically inspired feature enhancement framework for ZSL. Specifically, we design a dual-channel learning framework that uses auxiliary data sets to enhance the feature extractor of the ZSL model and propose a novel method to guide the selection of the auxiliary data sets based on the knowledge of biological taxonomy. Extensive experimental results show that our proposed method can effectively improve the generalization ability of the ZSL model and achieve state-of-the-art results on three benchmark ZSL tasks. We also explained the experimental phenomena through the way of feature visualization. Copyright © 2020, The Authors. All rights reserved.

关键词： Taxonomies

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A Biologically Inspired Feature Enhancement Framework for Zero-Shot Learning

A Biologically Inspired Feature Enhancement Framework for Ze...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Zhongwu Xie Weipeng Cao Xizhao Wang Zhong Ming Jingjing Zhang Jiyong Zhang College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Command and Control Engineering Army Engineering University of People’s Liberation Army of China Nanjing China School of Automation Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9781728165509

ISBN: (纸本)9781728165516

Most of the Zero-Shot Learning (ZSL) algorithms currently use pre-trained models as their feature extractors, which are usually trained on ImageNet by using deep neural networks. The richness of the feature information embedded in the pre-trained models can help the ZSL model extract more useful features from its limited training data. However, sometimes the difference between the training data of the current ZSL task and the ImageNet is too large, which may cause the use of pre-trained models has no obvious help or even negative impact on the model performance. To solve this problem, this paper proposes a biologically inspired feature enhancement framework for ZSL. Specifically, we design a dual-channel learning framework that uses auxiliary data sets to enhance the feature extractor of the ZSL model and propose a novel method to guide the selection of the auxiliary data sets based on the knowledge of biological taxonomy. Extensive experimental results show that our proposed method can effectively improve the generalization ability of the ZSL model and achieve state-of-the-art results on three benchmark ZSL tasks. We also explained the experimental phenomena through the way of feature visualization.

关键词： Zero-shot learning feature enhancement feature transfer biological taxonomy ancillary data Respiratory Inspiration extraction device Learning Frameworks Training data Refractive surgery enhancement

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Learning Greenhouse Climate control Policy from Monitored Data

Learning Greenhouse Climate Control Policy from Monitored Da...

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Chinese Automation Congress (CAC)

作者： Xiaoxuan Zhao Haoyu Wang Xiujuan Wang Udom Lewlomphaisarl Dong Li Jing Hua Mengzhen Kang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technologies Beijing China National Electronics and Computer Technology Center Pathum Thani Thailand Institute of Digital Agriculture Zhejiang Academy of Agricultural Sciences Hangzhou China Qingdao AgriTech Co. Ltd. Qingdao China

The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration.

关键词： Ventilators Green buildings Buildings Production Ventilation Data models Data mining

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Electrical and Mechanical Sensor-Based Mass Flow Rate Measurement System: A Comparative Approach

Electrical and Mechanical Sensor-Based Mass Flow Rate Measur...

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International Conference on Robotics and Automation Sciences (ICRAS)

作者： Usama Abrar Liu Shi Nasif Raza Jaffri Michael Short Khawar Hasham School of Control and Computer Engineering North China Electric Power University Beijing 102206 China School of Computing Engineering and Digital Technologies Teesside University Middlesbrough Cleveland TS1 3BA UK Interactive Group Islamabad Pakistan

ISBN: (数字)9781728198576

ISBN: (纸本)9781728198583

Gas-Solid flow finds its application in various industries such as chemical and food processes, pharmaceuticals, automobile and power generation. The precise measurement and real-time monitoring of the flow process have garnered a lot of attraction in the modern industrial era. The effective utilization, with high efficiency, of the resources have compelled the researchers to investigate Gas-Solid flow monitoring and measurement process for the last two decades. Efforts are extended to design instruments for flow pattern visualization and quantification of mass flow rate. Generally, volumetric flow concentration and velocity profile of the solid particles are required to find the mass flow rate (MRF) in the case of non-invasive measurement techniques. This paper proposes a novel non-invasive mass flow rate measurement system for solid particles where electrical and mechanical sensors are simultaneously used to compensate each other's associated drawbacks. The solid particles are allowed to slide along an insulated inclined channel under the effect of gravity. The velocity profile and volumetric flow concentration are calculated using electrical sensors. Furthermore, load cell serves as a second sensor to calculate the mass flow rate. The experimental results from both the sensors are compared to investigate the accuracy and relative errors of both the sensors under different conditions.

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control design and Lyapunov Functions via Bernstein Approximations: Exact Results ⁎

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IFAC-PapersOnLine 2020年第2期53卷 6459-6464页

作者： Tareq Hamadneh Nikolaos Athanasopoulos Rafael Wisniewski Al Zaytoonah University of Jordan Department of Mathematics Amman Jordan School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Northern Ireland Section of Automation and Control Aalborg University 9220 Aalborg East Denmark

We study the control problem for polynomial continuous-time dynamical systems. We consider polynomial Lyapunov functions and controllers, both parameterised in Bernstein form. Specifically, we present necessary and sufficient conditions for existence of polynomial controllers and Lyapunov functions of some maximum degree, providing at the same time explicit upper bounds on the degree of the involved Bernstein polynomials. The formulated conditions are a set of algebraic inequalities, in the space of Bernstein coefficients.

关键词： Bernstein polynomials control design certificates of positivity Lyapunov stability control Lyapunov Functions polynomial systems

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Automated osteoporosis classification and T-score prediction using hip radiographs via deep learning algorithm

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Therapeutic Advances in Musculoskeletal Disease 2024年 16卷

作者： Chen, Yu-Pin Chan, Wing P. Zhang, Han-Wei Tsai, Zhi-Ren Peng, Hsiao-Ching Huang, Shu-Wei Jang, Yeu-Chai Kuo, Yi-Jie Department of Orthopedics Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Department of Radiology Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Biomedica Corporation New Taipei City Taiwan Program for Aging China Medical University Taichung City Taiwan Institute of Population Health Sciences National Health Research Institutes Miaoli County Taiwan Department of Electrical and Computer Engineering Institute of Electrical Control Engineering National Yang Ming Chiao Tung University Hsinchu County Hsinchu City Taiwan Department of Computer Science and Information Engineering Asia University Taichung City Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung City Taiwan Center for Precision Medicine Research Asia University Taichung City Taiwan Department of Applied Science National Taitung University Taitung County Taitung City Taiwan Department of Orthopedics Wan Fang Hospital Taipei Medical University No. 111 Sec. 3 Xinglong Road Wenshan Taipei 11696 Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

Background: Despite being the gold standard for diagnosing osteoporosis, dual-energy X-ray absorptiometry (DXA) is an underutilized screening tool for osteoporosis. Objectives: This study proposed and validated a controllable feature layer of a convolutional neural network (CNN) model with a preprocessing image algorithm to classify osteoporosis and predict T-score on the proximal hip region via simple hip radiographs. Design: This was a single-center, retrospective study. Methods: An image dataset of 3460 unilateral hip images from 1730 patients (age ⩾50 years) was retrospectively collected with matched DXA assessment for T-score for the targeted proximal hip regions to train (2473 unilateral hip images from 1430 patients) and test (497 unilateral hip images from 300 patients) the proposed CNN model. All images were processed with a fully automated CNN model, X1AI-Osteo. Results: The proposed screening tool illustrated a better performance (sensitivity: 97.2%;specificity: 95.6%;positive predictive value: 95.7%;negative predictive value: 97.1%;area under the curve: 0.96) than the open-sourced CNN models in predicting osteoporosis. Moreover, when combining variables, including age, body mass index, and sex as features in the training metric, there was high consistency in the T-score on the targeted hip regions between the proposed CNN model and the DXA (r = 0.996, p < 0.001). Conclusion: The proposed CNN model may identify osteoporosis and predict T-scores on the targeted hip regions from simple hip radiographs with high accuracy, highlighting the future application for population-based opportunistic osteoporosis screening with low cost and high adaptability for a broader population at risk. Trial registration: TMU-JIRB N201909036. © The Author(s), 2024.

关键词： deep learning neural network osteoporosis radiographs T-score

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Multi-Step Ahead Time Series Forecasting Based on the Improved Process Neural Networks

Multi-Step Ahead Time Series Forecasting Based on the Improv...

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作者： Haijian Shao Chunlong Hu Xing Deng Dengbiao Jiang Shuonan Hou School of Computer Science and Engineering Jiangsu University of Science and Technology School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast University Key Laboratory of Smart Grid of Ministry of Education Tianjin University

This paper proposes a multi-step ahead time series forecasting based on the improved process neural *** intelligent algorithm particle swarm optimization(PSO) is used to overcome the potential disadvantages of the neural network,such as slow convergence speed and derivative local ***,the theoretical analysis of the PSO is given to optimize the Multilayer perceptron(MLP) neural network ***,the theoretical analysis and processing flow about the MLP architecture optimization is ***,the performance criteria are applied to verify the performance of the proposed ***,the experimental evaluation based on a typically chaotic time series with rich spectrum information is utilized to demonstrate that the proposed approach has comparative results and superior on forecasting accuracy comparing to the traditional methods.

关键词： Time series analysis Multilayer perceptron Particle swarm optimization

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A Relay Satellite-Assisted Covert Communication Position Optimization in 6G Satellite Communication

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Electronics Letters 2025年第1期61卷

作者： Wanxin Ma Wen Tian Huaifeng Shi Xufei Ding Xin Sun Shuhua Huang CountryKey Laboratory of Intelligent Support Technology In Complex Environment (Nanjing University of Information Science and Technology) Ministry of Education Nanjing University of Information Science and Technology Nanjing China The College of Computer Science and Software Engineering Hohai University Nanjing China School of Automation Nanjing University of Science and Technology Nanjing China The College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

Satellite communication, a key technology in the 6G communication system, has advantages such as wide coverage, being unrestricted by geographical conditions, and large communication capacity. However, the communication process timely face eavesdropping from illegal warden due to the wireless broadcast characteristics of satellite communication. To solve this problem, we propose a relay satellite-assisted covert communication on position optimization mechanics (RSCCPOM). Specifically, we construct a satellite communication countermeasure model and obtain the minimum detection error probability for the warden, and then optimize the position of relay satellite. Simulation results show that our RSCCPOM improve covert communication rate compared with existing work.

关键词： satellite communication wireless communications

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