检索结果-内蒙古大学图书馆

Dynamic Analysis of a Hyperchaotic Hyperjerk Circuit, FPGA Implementation and Its Application in RNG for Medical Images Encryption

引用

Transactions of the Indian National Academy of engineering 2022年第3期7卷 753-773页

作者： Feudjio, E. R. Tamba, V. Kamdoum Tagne, F. Kapche Tchendjeu, A. E. Tchahou Djatche, G. P. Biamou, A. L. Mbanda Fotsin, H. B. Research Unit of Automation and Applied Computer (URAIA) Department of Electrical Engineering IUT-FV Bandjoun University of Dschang Bandjoun Cameroon Research Unit of Condensed Matter Electronics and Signal Processing (URMACETS) Department of Physics University Of Dschang Dschang Cameroon Department of Computer Engineering National Polytechnic Institute University of Bamenda Bambili Cameroon

Nonlinear systems with high dimension have received considerable attention recently due to their possibility to exhibit complex dynamics including chaos, hyperchaos, pattern formation, self-organization, and other related dynamics. In this work, a novel four dimensional autonomous hyperchaotic hyperjerk circuit with hyperbolic sine and quintic nonlinearities is proposed and its corresponding dynamics is analyzed theoretically, numerically and experimentally. Using Kirchhoff’s laws, the state equations of the proposed hyperchaotic hyperjerk circuit are described. Some fundamental properties of the model including symmetry, dissipation, equilibrium points and their stability are examined. Bifurcation diagrams, Lyapunov exponent spectrum and phase portraits are plotted to point out the complex dynamical behaviors exhibited by the system. The numerical results are validated using PSpice-based circuit simulations and Field-Programmable Gate Array (FPGA) implementations. In addition, to use the proposed hyperchaotic hyperjerk circuit for medical images encryption, a random number generator (RNG) is designed. The resulting random number generator (RNG) successfully passes the National institute of Standards and Technology (NIST-800-22) statistical tests. This confirms that the proposed hyperchaotic hyperjerk circuit is effective in showing randomness. Finally, a medical images encryption algorithm is developed based on the generated random bits. The security performances analyses are performed and the results show that the proposed algorithm can effectively encrypt and decrypt medical images with better security performances.

关键词：

来源：评论

学校读者我要写书评

暂无评论

On Task-Relevant Loss Functions in Meta-Reinforcement Learning and Online LQR

arXiv

引用

arXiv 2023年

作者： Shin, Jaeuk Kim, Giho Lee, Howon Han, Joonho Yang, Insoon The Department of Electrical and Computer Engineering Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Artificial Intelligence ASRI Seoul National University Seoul08826 Korea Republic of

Designing a competent meta-reinforcement learning (meta-RL) algorithm in terms of data usage remains a central challenge to be tackled for its successful real-world applications. In this paper, we propose a sample-efficient meta-RL algorithm that learns a model of the system or environment at hand in a task-directed manner. As opposed to the standard model-based approaches to meta-RL, our method exploits the value information in order to rapidly capture the decision-critical part of the environment. The key component of our method is the loss function for learning the task inference module and the system model that systematically couples the model discrepancy and the value estimate, thereby facilitating the learning of the policy and the task inference module with a significantly smaller amount of data compared to the existing meta-RL algorithms. The idea is also extended to a non-meta-RL setting, namely an online linear quadratic regulator (LQR) problem, where our method can be simplified to reveal the essence of the strategy. The proposed method is evaluated in high-dimensional robotic control and online LQR problems, empirically verifying its effectiveness in extracting information indispensable for solving the tasks from observations in a sample efficient manner. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Antigen-specific antibody design via direct energy-based preference optimization 24

Antigen-specific antibody design via direct energy-based pre...

引用

Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Xiangxin Zhou Dongyu Xue Ruizhe Chen Zaixiang Zheng Liang Wang Quanquan Gu School of Artificial Intelligence University of Chinese Academy of Sciences and New Laboratory of Pattern Recognition (NLPR) State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences (CASIA) and ByteDance Research ByteDance Research College of Computer Science and Electronic Engineering Hunan University and ByteDance Research School of Artificial Intelligence University of Chinese Academy of Sciences and New Laboratory of Pattern Recognition (NLPR) State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences (CASIA)

ISBN: (纸本)9798331314385

Antibody design, a crucial task with significant implications across various disciplines such as therapeutics and biology, presents considerable challenges due to its intricate nature. In this paper, we tackle antigen-specific antibody sequence-structure co-design as an optimization problem towards specific preferences, considering both rationality and functionality. Leveraging a pre-trained conditional diffusion model that jointly models sequences and structures of antibodies with equivariant neural networks, we propose direct energy-based preference optimization to guide the generation of antibodies with both rational structures and considerable binding affinities to given antigens. Our method involves fine-tuning the pre-trained diffusion model using a residue-level decomposed energy preference. Additionally, we employ gradient surgery to address conflicts between various types of energy, such as attraction and repulsion. Experiments on RAbD benchmark show that our approach effectively optimizes the energy of generated antibodies and achieves state-of-the-art performance in designing high-quality antibodies with low total energy and high binding affinity simultaneously, demonstrating the superiority of our approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Fault Diagnosis of Uncertain Grid-Connected Photovoltaic systems using Deep GRU-based Bayesian optimization

引用

IFAC-PapersOnLine 2024年第4期58卷 449-454页

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine 1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Tunisia

The efficacy of photovoltaic systems is significantly impacted by electrical production losses attributed to faults. Ensuring the rapid and cost-effective restoration of system efficiency necessitates robust fault detection and diagnosis (FDD) procedures. This study introduces a novel interval-gated recurrent unit (I-GRU) based Bayesian optimization framework for FDD in grid-connected photovoltaic (GCPV) systems. The utilization of an interval-valued representation is proposed to address uncertainties inherent in the systems, the GRU is employed for fault classification, while the Bayesian algorithm optimizes its hyperparameters. Addressing uncertainties through the proposed approach enhances monitoring capabilities, mitigating computational and storage costs associated with sensor uncertainties. The effectiveness of the proposed approach for FDD in GCPV systems is demonstrated using experimental application.

关键词： Bayesian optimization fault detection fault diagnosis gated recurrent units grid-connected PV systems interval-data representation uncertainties

来源：评论

学校读者我要写书评

暂无评论

Surrogate-Assisted Multi-Class Collaborative Teaching and Learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Jing Bi Ziqi Wang Haitao Yuan Jinhong Yang Jia Zhang College of Computer Beijing University of Technology Beijing China Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Lyle School of Engineering Southern Methodist University Dallas TX USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions.

关键词： Accuracy Processor scheduling Computational modeling Education Autoencoders Collaboration Search problems Space exploration Particle swarm optimization Optimization

来源：评论

学校读者我要写书评

暂无评论

Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

引用

Neuroscience Bulletin 2023年第6期39卷 893-910页

作者： Feng Su Yangzhen Wang Mengping Wei Chong Wang Shaoli Wang Lei Yang Jianmin Li Peijiang Yuan Dong-Gen Luo Chen Zhang Department of Neurobiology School of Basic Medical SciencesBeijing Key Laboratory of Neural Regeneration and RepairCapital Medical UniversityBeijing100069China Chinese Institute for Brain Research Beijing102206China State Key Laboratory of Translational Medicine and Innovative Drug Development Nanjing210000China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing100871China School of Life Sciences Tsinghua UniversityBeijing100084China School of Biological Science and Medical Engineering Beihang UniversityBeijing100191China The Key Laboratory of Developmental Genes and Human Disease Institute of Life SciencesSoutheast UniversityNanjing210096JiangsuChina Institute for Artificial Intelligence the State Key Laboratory of Intelligence Technology and SystemsBeijing National Research Center for Information Science and TechnologyDepartment of Computer Science and TechnologyTsinghua UniversityBeijing100084China School of Mechanical Engineering and Automation Beihang UniversityBeijing100191China

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social *** tracking methods(e.g.,marking each animal with dye or surgically implanting microchips)can be invasive and may have an impact on the social behavior being *** overcome these shortcomings,video-based methods for tracking unmarked animals,such as fruit flies and zebrafish,have been ***,tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction *** this study,we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network(R-CNN)deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion *** system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing *** proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.

关键词： Noninvasive tracking Deep learning Multi-camera Mouse group Social interaction

来源：评论

学校读者我要写书评

暂无评论

An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron

arXiv

引用

arXiv 2024年

作者： Alazab, Moutaz Khurma, Ruba Abu Castillo, Pedro A. Abu-Salih, Bilal Martı´n, Alejandro Camacho, David Faculty of Artificial Intelligence Al-Balqa Applied University Amman Jordan Computer Science Department Faculty of MEU Research Unit Middle East University Amman Jordan Department of Computer Engineering Automation and Robotics University of Granada Granada Spain King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XGBoost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%. © 2024, CC BY.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

ALIM: adjusting label importance mechanism for noisy partial label learning 23

ALIM: adjusting label importance mechanism for noisy partial...

引用

Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Mingyu Xu Zheng Lian Lei Feng Bin Liu Jianhua Tao The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Computer Science and Engineering Nanyang Technological University Department of Automation Tsinghua University and Beijing National Research Center for Information Science and Technology Tsinghua University

Noisy partial label learning (noisy PLL) is an important branch of weakly supervised learning. Unlike PLL where the ground-truth label must conceal in the candidate label set, noisy PLL relaxes this constraint and allows the ground-truth label may not be in the candidate label set. To address this challenging problem, most of the existing works attempt to detect noisy samples and estimate the ground-truth label for each noisy sample. However, detection errors are unavoidable. These errors can accumulate during training and continuously affect model optimization. To this end, we propose a novel framework for noisy PLL with theoretical interpretations, called "Adjusting Label Importance Mechanism (ALIM)". It aims to reduce the negative impact of detection errors by trading off the initial candidate set and model outputs. ALIM is a plug-in strategy that can be integrated with existing PLL approaches. Experimental results on multiple benchmark datasets demonstrate that our method can achieve state-of-the-art performance on noisy PLL. Our code is available at: https://***/zeroQiaoba/ALIM.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Heart Failure: Machine Learning Prediction Within a 5-Year Framework

Heart Failure: Machine Learning Prediction Within a 5-Year F...

引用

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Konstantina-Helen Tsarapatsani Vasilios D. Tsakanikas Antonis Sakellarios Hans J. Trampisch Efterpi Karapintzou Henrik Rudolf George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greeece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina and Biomedical Research Institute-FORTH Ioannina Greece

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction.

关键词： Accuracy Sensitivity Explainable AI Biological system modeling Peptides Receivers Predictive models Indexes Cardiovascular diseases Random forests

来源：评论

学校读者我要写书评

暂无评论

MGDefect: A Mask-Guided High-Quality Defect Image Generation Method for Improving Defect Inspection

引用

IEEE Transactions on Multimedia 2025年

作者： Jiang, Xiaoheng Li, Yingjie Yan, Feng Lu, Yang Xu, Changsheng Xu, Mingliang Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China National Supercomputing Center in Zhengzhou Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou450001 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China

Deep learning based defect inspection methods have achieved promising performance, which usually relies on a large number of well-labeled training samples. However, it requires much effort to obtain enough annotated samples especially pixel-level annotations in practical production. Generative adversarial networks (GANs) can be utilized to generate defect samples. However, training GANs typically requires a large amount of defect data and most of them cannot generate defect samples with pixel-level annotations. In this paper, we present a Mask-Guided Defect image generation method, called MGDefect, which can generate high-quality defect samples with pixel-level annotations and effectively improves the performance of downstream tasks. Specifically, MGDefect consists of a Mask-Guided Defect Generation GAN (MGDG-GAN) and a Defect Mask GAN (DM-GAN). MGDG-GAN generates images containing defects with specific locations, shapes, and sizes via mask guidance and the dual discrimination for defects at the region level and image level. DM-GAN aims to generate diverse and rational masks for MGDG-GAN. It also adopts region-level and image-level dual discrimination for masks to generate compatible masks with the target objects. MGDG-GAN mainly focuses on generating local defect regions and DM-GAN specializes in generating masks, which are both trained on limited defect samples and abundant normal samples. Experiments conducted on the MVTec AD, DAGM 2007, and KolektorSDD2 benchmark datasets demonstrate that our method achieves promising results compared with other state-of-the-art approaches. Meanwhile, the generated defect samples significantly improve the performance of defect inspection tasks including classification and segmentation. Specifically, our method achieves KIDx 103/IS scores of 48.35/2.27 on MVTec AD, 15.37/2.44 on DAGM 2007, and 19.70/2.01 on KolektorSDD2. Furthermore, our method improves mIoU by 10.59%, 2.20%, and 2.17% on these datasets, respectively, using U-Net

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：