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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network 2

Identification of Age, Plumage, and Sex of Bird Species Usin...

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Sourik Gupta, Richa Pandian, J. Arun Saranya, S. Mariappan, L. Thanga Vellore Institute of Technology Department of Computer Application Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350306415

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset. © 2024 IEEE.

关键词： Bird Plumage Identification Bird Species Identification Deep Convolutional Neural Networks Image Augmentation Pre-trained networks

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Malaria and Pneumonia Disease Prediction Using Deep Learning 2

Malaria and Pneumonia Disease Prediction Using Deep Learning

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Vijendra Singhal, Manoj Dwivedy, Bhoopendra Jain, Pallavi Ritika Abes Institute of Technology Department of Information Technology Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Information Technology Greater Noida India School of Computer Science and Engineering Galgotia's University Greater Noida India School of Computer Application and Technology Galgotia's University Greater Noida India

ISBN: (纸本)9798350371055

Early identification, intervention, and efficient treatment planning all rely heavily on disease prediction. This study examines the methods for disease prediction based on deep learning, concentrating on pneumonia and malaria in particular. Pneumonia and malaria are two common, sometimes fatal infections, especially in underdeveloped areas. Traditional diagnostic techniques for these disorders can be time-consuming and frequently need for specialized knowledge. Therefore, it is necessary to investigate automated and reliable illness prediction methods. This study's goal is to assess how well deep learning algorithms can foresee malaria and pneumonia from medical imaging data. The study makes use of a broad dataset that includes chest X-ray pictures for the identification of pneumonia and blood smear images with malaria infection. To improve the dataset's quality and variety, preprocessing procedures including picture normalization and augmentation are used. The results of this research demonstrate the potential of deep learning algorithms for malaria and pneumonia prediction, which advances the field of medical diagnostics. These automated prediction algorithms could help medical personnel diagnose patients quickly, allowing for quick treatment to start and better patient outcomes. © 2024 IEEE.

关键词： Backpropagation CNN Deep Learning Inspection V3 Model Jupiter Python Transfer Learning

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Research on Model Predictive Control-Based Feedback Strategies for Autonomous Driving 6

Research on Model Predictive Control-Based Feedback Strategi...

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6th IEEE Advanced information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Li, Ting Xu, Xianxia Liu, Xuejuan Liu, Jie Guangxi Vocational Normal University School of Computer and Information Engineering Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China

ISBN: (纸本)9798350316520

To address the issues of control stability and safety in autonomous vehicles, this study focuses on research related to Model Predictive Control (MPC) algorithms and algorithm optimization. By leveraging MPC control algorithms and system dynamics techniques, we have achieved stable autonomous driving. Simulation experiments based on Model Predictive Control have been conducted to validate the feasibility and effectiveness of MPC algorithms for feedback control in autonomous driving. Two distinct simulation scenarios have been designed to verify the MPC algorithm's performance under different road conditions. The results indicate that the MPC algorithm effectively enhances the control stability and safety of autonomous driving vehicles. © 2024 IEEE.

关键词： Model predictive control

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Machine Learning-Based Methods for Materials Inverse Design: A Review

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computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with Engineering applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Distributed Aperture Coherent Synthesis Method based on Two-stage Compensation 2

Distributed Aperture Coherent Synthesis Method based on Two-...

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2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Liu, Zhongwei Zhou, Dongming Xiang, Rongrong Chen, Jie Wu, Tianhang Ding, Tianyu Liu, Xiaochuan Gao, Hongwei Beihang University School of Electronic and Information Engineering Beijing China School of Information and Electronics Beijing Institute of Technology Radar Research Lab Beijing China Beijing Institute of Computer Technology and Application Department of Data Technology and Application Beijing China Beijing Institute of Radio Measurement Radar R&d Center Beijing China

ISBN: (纸本)9798331515669

To solve the problem that it is difficult to accurately estimate the coherent parameters of distributed aperture radar under the condition of low signal-to-noise ratio, a distributed coherent synthesis method based on two-stage compensation is proposed in this paper. This method utilizes prior information such as system layout, beam pointing, and inertial navigation station location to perform coarse alignment of echoes at the first stage in time, and then uses cross-correlation method to estimate coherent parameters of delay and phase within a small range, and perform precise compensation of time and phase at the second stage, so as to realize the coherent synthesis of multiple radar echoes. Experimental results show that the proposed method can effectively realize distributed coherent synthesis processing under low signal-to-noise ratio. © 2024 IEEE.

关键词： Signal to noise ratio

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Developing microwave-induced thermoacoustic tomography:System,application,and reconstruction

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Journal of Electronic Science and technology 2023年第4期21卷 1-19页

作者： Shuang-Li Liu Wan-Ting Peng Xiao-Zhang Zhu Jian Song Xin Shang Hua Zhang Zhi-Qin Zhao School of Information Engineering Southwest University of Science and TechnologyMianyang621010China School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China

A microwave-induced thermoacoustic imaging(MITAT)system is a non-destructive physical medical imaging method that combines the advantages of the high contrast of microwave imaging and the high resolution of ultrasound *** uses the microwave as the excitation source and ultrasound as the information *** different kinds of biological tissue absorb electromagnetic energy,it results in localized temperature *** thermal expansion will induce ultrasonic signals(i.e.,thermoacoustic signals),known as the thermoacoustic *** microwave absorption image of the sample can be reconstructed by algorithm *** MITAT contrast depends on different dielectric parameters of different kinds of *** introduce the developed system and its *** addition,the challenges and prospects of MITAT for further development are discussed.

关键词： application Microwave-induced thermoacoustic tomography(MITAT) Reconstruction System

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Multi-scale Feature Fusion for Enhanced Person Re-identification 3rd

Multi-scale Feature Fusion for Enhanced Person Re-identific...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Wang, XiaoHui Sun, Yulin Zou, Zhipeng Liang, Xiaoyang Sun, Zhenyu Liu, Wei Shandong Business Institute Yantai China Shandong Xuewen Information Technology Co. Ltd. Jining China Technology of Computer Application Yantai Engineering and Technology College Yantai China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (纸本)9789819624676

In the realm of surveillance and security, person re-identification (Re-ID) stands as a critical challenge, particularly across non-overlapping camera networks. The essence of this challenge lies in the accurate and efficient identification of individuals by leveraging features captured from multiple perspectives. Addressing this, we introduce the Contextual Multi-Scale Network (Asy-CMSNet), a novel framework designed to enhance the Re-ID process through the integration of asymmetrical convolution and an innovative loss function, the Enhanced TriHard (E-TriHard). Unlike traditional approaches that rely on the stacking of convolutional layers, Asy-CMSNet employs a sophisticated mechanism that allows for the nuanced extraction and integration of multi-scale features, thereby optimizing the identification process across diverse pedestrian appearances. Central to our framework is the notion that effective person Re-ID necessitates the consideration of both global and localized pedestrian details, ranging from broad clothing characteristics to subtle accessories, which our model adeptly captures and utilizes. Through extensive experimentation across several benchmark datasets, Asy-CMSNet demonstrates superior performance in person Re-ID challenges, highlighting a substantial progression in the area. Our findings not only set new performance benchmarks but also pave the way for future research into more efficient, scalable, and robust person Re-ID systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Benchmarking

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NOMA in Flexible Optical Access Network: Principle, Advantage and Challenges 21

NOMA in Flexible Optical Access Network: Principle, Advantag...

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21st International Conference on Optical Communications and Networks, ICOCN 2023

作者： Chai, Jia Feng, Nan Ma, Mingyi Li, Zhe Li, Shaobo Beijing Institute of Computer Technology and Application China The 54th Research Institute of Cetc Shijiazhuang050081 China Hebei Key Laboratory of Photonic Information Technology and Application Shijiazhuang050081 China

We summarize the current principle, advantage, and challenges of power domain non-orthogonal multiple access technology which exploited the benefits of different channel gains of passive optical network to improve the... 详细信息

ISBN: (纸本)9798350343502

关键词： Passive optical networks

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