检索结果-内蒙古大学图书馆

A Smarter Pavlovian Dog with Optically Modulated Associative Learning in an Organic Ferroelectric Neuromem

Research 2021年第1期2021卷 1148-1157页

作者： Mengjiao Pei Changjin Wan Qiong Chang Jianhang Guo Sai Jiang Bowen Zhang Xinran Wang Yi Shi Yun Li National Laboratory of Solid-State Microstructures School of Electronic Science and EngineeringCollaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China School of Computing Tokyo Institute of TechnologyTokyo 152-8550Japan School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213164China

Associative learning is a critical learning principle uniting discrete ideas and percepts to improve individuals’***,enabling high tunability of the association processes as in biological counterparts and thus integration of multiple signals from the environment,ideally in a single device,is ***,we fabricate an organic ferroelectric neuromem capable of monadically implementing optically modulated associative *** approach couples the photogating effect at the interface with ferroelectric polarization switching,enabling highly tunable optical modulation of charge *** device acts as a smarter Pavlovian dog exhibiting adjustable associative learning with the training cycles tuned from thirteen to *** particular,we obtain a large output difference(>10^(3)),which is very similar to the all-or-nothing biological sensory/motor neuron spiking with decrementless *** proof-of-concept demonstrations,photoferroelectric coupling-based applications in cryptography and logic gates are achieved in a single device,indicating compatibility with biological and digital data processing.

关键词： ferroelectric associative proof

来源：评论

学校读者我要写书评

暂无评论

LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Wang, Ze Yang, Kailun Shi, Hao Zhang, Yufan Xu, Zhijie Gao, Fei Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China School of Computing and Engineering University of Huddersfield Huddersfield U.K State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

In this paper, we propose LF-PGVIO, a Visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. The purpose of our research is to unleash the potential of point-line odometry with large-FoV omnidirectional cameras, even for cameras with negative-plane FoV. To achieve this, we propose an Omnidirectional Curve Segment Detection (OCSD) method combined with a camera model which is applicable to images with large distortions, such as panoramic annular images, fisheye images, and various panoramic images. The geodesic segment is sliced into multiple straight-line segments based on the radian and descriptors are extracted and recombined. Descriptor matching establishes the constraint relationship between 3D line segments in multiple frames. In our VIO system, line feature residual is also extended to support large-FoV cameras. Extensive evaluations on public datasets demonstrate the superior accuracy and robustness of LF-PGVIO compared to state-of-the-art methods. The source code will be made publicly available at https://***/flysoaryun/LF-PGVIO. IEEE

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Multiview Top-k Pooling with Deep-Q-Networks

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2985-2996页

作者： Li, Zhi-Peng Su, Hai-Long Wu, Yong- Zhang, Qin-Hu Yuan, Chang-An Gribova, Valeriya Filaretov, Vladimir Fedorovich Huang, De-Shuang Eastern Institute of Technology Zhejiang Ningbo315201 China University of Science and Technology of China School of Life Sciences Anhui Hefei230026 China Tongji University Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Shanghai201804 China Guangxi Academy of Sciences Institute of Big Data and Intelligent Computing Research Center Nanning530007 China Far Eastern Branch of the Russian Academy of Sciences Institute of Automation and Control Processes Vladivostok690041 Russia

Graph neural networks (GNNs) are extensions of deep neural networks to graph-structured data. It has already attracted widespread attention for various tasks such as node classification and link prediction. Existing research focuses more on graph convolutional neural networks (GCNs). However, it is usually overlooked that graph pooling can obtain graph representations by summarizing and down-sampling node information. Meanwhile, existing graph pooling methods mainly use top-k for node selection, but most of them consider only single view information when scoring nodes, and the k values in top-k are usually selected empirically. This work proposes the hierarchical multiview top-k pooling (HMTPool) with deep-Q-networks, which scores nodes taking into account multiview information (considering graph structure and features) and does not rely on the empirical adaptive selection of the best k value. HMTPool is a two-stage process. It first uses a variant GCN and multilayer perceptron to score the nodes from structural and feature perspectives, respectively and then performs fusion operations on the multiview information scores of the nodes. In addition, to select the optimal pooling ratio of top-k, we propose a deep-Q-network-based top-k (DTop-k) node selection method, which can adaptively select the best pooling ratio without prior knowledge. Experimental results on six TUDatasets and two Benchmarking GNNs datasets demonstrate the effectiveness of our proposed approach. © 2020 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

IRS-Aided Mobile Edge computing for Mine IoT Networks using Deep Reinforcement Learning

IRS-Aided Mobile Edge Computing for Mine IoT Networks using ...

引用

2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Zhang, Peng Min, Minghui Xiao, Jiayang Li, Shiyin Zhang, Hongliang China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xuzhou First People's Hospital Xuzhou221116 China Ministry of Education School of CyberScience and Engineering Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China Peking University School of Electronics Beijing100871 China

ISBN: (纸本)9798350345384

Recent years have witnessed a growing interest in implementing mobile edge computing (MEC) into mine Internet of Things (IoT) networks to enable real-time monitoring and safety management. However, the spatial blockage and limited transmitting power pose challenges to signal coverage and efficient computation offloading. To address these issues, we propose incorporating intelligent reflecting surfaces (IRSs) in the MEC system for underground mines (MMEC) to resolve wireless coverage problems in mine communication scenarios and enhance the channel gains of offloading links. Given the time-varying and complex channel conditions in mines and the multiple coupled variables involved in IRSs' reflecting coefficient matrix, we utilize Deep Reinforcement Learning (DRL) to acquire the optimal IRS-assisted computation offloading strategy in dynamic mine environments. Specifically, we propose a Deep Deterministic Policy Gradient (DDPG)-based IRS-aided computation offloading scheme to jointly optimize the IRS elements' reflection coefficients and the computation offloading rate. Simulation results demonstrate that introducing an IRS into the MMEC system yields outstanding benefits and significantly exceeds benchmarks in enhancing energy efficiency and decreasing computational latency. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Quaternion vs Euler-based Approaches in Reduced INS/GNSS Fusion Techniques

Performance Analysis of Quaternion vs Euler-based Approaches...

引用

2023 International Telecommunications Conference, ITC-Egypt 2023

作者： Tarek, Maher Zahran, Shady Radi, Ahmed Nafea, Sherif F Oda, Eyad S. Hafez, Alaa El-Din Sayed Technical Researches and Development Center Control and Navigation Departement Cairo Egypt Arab Academy for Science Technology and Maritime Transport College of Computing and Information Technology Cairo Egypt Technical Researches Center Cairo Egypt Suez Canal University Electrical Engineering Department Cairo Egypt Alexandria University Electrical Engineering Affiliate Instructor Cairo Egypt

ISBN: (纸本)9798350326062

Unmanned vehicle navigation using various combination of sensors in different fields, civilian or military, has always known to be a challenging and nontrivial task. Bayesian filter algorithms are usually being used for combining and optimally processing measurements provided by such sensors, considering the available knowledge such as the uncertainty in measurements. Since the performance of any navigation system mainly depends on the efficiency of the chosen filter algorithm associated with the utilized sensors' quality, the choice of suitable filtering approach (according to the demand application) can be considered as a critical and essential designing point. In this paper, the performance of two different attitudes representation filtering approaches (Quaternion and Euler angles-based) is investigated, when applied in Multiplicative Extended Kalman Filter (MEKF) design. The overall performance and accuracy of both approaches were analyzed and compared during various GNSS signal outages for a self-driving cars application in straight and curved trajectory paths. Multiple in-filed experiments were conducted using a special setup that includes a MEMS-based SPATIAL Advanced Navigation INS/GNSS system where the output of both approaches was compared to the ground truth of a utilized reference navigation system. The comparison includes 1) Position, Velocity and Attitude (PVA) estimation accuracy, 2) processing time and computational complexity. Results concluded that each approach has its own merits in demerits where the Quaternion-based approach outperforms the Euler one in terms of PVA navigation solution estimation accuracy. On the other hand, a faster calculation for the Euler-based approach associated with lower computational complexity was significantly noticeable. © 2023 IEEE.

关键词： Inertial navigation systems

来源：评论

学校读者我要写书评

暂无评论

Optimizing Human Activity Recognition Using Stacked CNN with Ensemble Learning

引用

Procedia Computer Science 2025年 258卷 1598-1607页

作者： Divya Yadav Deepika Rani Om Parakash Verma Department of Mathematics and Computing Dr B R Ambedkar National Institute of Technology Jalandhar Jalandhar Punjab India Department of Instrumentation and Control Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Jalandhar Punjab India

In recent times, Human Activity Recognition (HAR) plays a crucial role in numerous artificial intelligence applications, including surveillance, healthcare, and security. However, the primary challenges in accurately identifying human activities from video data include occlusion, low resolution, data scarcity, and high computational costs. The main objective of this study is to enhance both the recognition accuracy and computational efficiency. This study presents a computationally efficient HAR model that leverages deep learning and machine learning algorithms for feature extraction and classification tasks. The proposed model consists of three fine-tuned convolution neural networks: VGG16, VGG19, and MobileNetV2 for feature extraction, combined with four classifiers, Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Fuzzy classifier. The performance of the proposed model was evaluated using three benchmark datasets: KTH, YouTube11, and Peliculas. The proposed method demonstrated exceptional performance of 99.60% and 99.50% accuracy on the KTH and Peliculas dataset using MobileNet and SVM. Additionally, the proposed method achieved a superior performance of 99.80% on the YouTube11 dataset with a combination of VGG16 and SVM. Finally, the performance of the proposed model was compared and validated against previous models.

关键词： Human Activity Recognition (HAR) Convolution Neural Network (CNN) Feature Extraction Classification

来源：评论

学校读者我要写书评

暂无评论

Learning to Learn Transferable Generative Attack for Person Re-Identification

引用

IEEE Transactions on Image Processing 2025年 PP卷 PP页

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning-based person re-identification (reid) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based crossmodel&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 20.0% and 11.3% on mean mAP drop rate, respectively. © 1992-2012 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Modality Unified Attack for Omni-Modality Person Re-Identification

引用

IEEE Transactions on Information Forensics and Security 2025年

作者： Bian, Yuan Liu, Min Yi, Yunqi Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning based person re-identification (re-id) models have been widely employed in surveillance systems. Recent studies have demonstrated that black-box single-modality and cross-modality re-id models are vulnerable to adversarial examples (AEs), leaving the robustness of multi-modality re-id models unexplored. Due to the lack of knowledge about the specific type of model deployed in the target black-box surveillance system, we aim to generate modality unified AEs for omni-modality (single-, cross- and multi-modality) re-id models. Specifically, we propose a novel Modality Unified Attack method to train modality-specific adversarial generators to generate AEs that effectively attack different omni-modality models. A multi-modality model is adopted as the surrogate model, wherein the features of each modality are perturbed by metric disruption loss before fusion. To collapse the common features of omnimodality models, Cross Modality Simulated Disruption approach is introduced to mimic the cross-modality feature embeddings by intentionally feeding images to non-corresponding modality-specific subnetworks of the surrogate model. Moreover, Multi Modality Collaborative Disruption strategy is devised to facilitate the attacker to comprehensively corrupt the informative content of person images by leveraging a multi modality feature collaborative metric disruption loss. Extensive experiments show that our MUA method can effectively attack the omni-modality re-id models, achieving 55.9%, 24.4%, 49.0% and 62.7% mean mAP Drop Rate, respectively. © 2005-2012 IEEE.

关键词： Modality unified attack Multi Modality Collaborative Disruption Omni-Modality re-id

来源：评论

学校读者我要写书评

暂无评论

A practical PID variable stiffness control and its enhancement for compliant force-tracking interactions with unknown environments

引用

Science China(Technological Sciences) 2023年第10期66卷 2882-2896页

作者： ZHANG Xin ZHOU Hao LIU JinGuo JU ZhaoJie LENG YuQuan YANG ChenGuang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China School of Computing University of PortsmouthPortsmouth PO13HEUK Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy EngineeringSouthern University of Science and TechnologyShenzhen 518055China Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Compliant interaction control is a key technology for robots performing contact-rich manipulation *** design of the compliant controller needs to consider the robot hardware because complex control algorithms may not be compatible with the hardware performance,especially for some industrial robots with low bandwidth *** paper focuses on effective and easy-to-use compliant control algorithms for position/velocity-controlled *** by human arm stiffness adaptation behavior,a novel variable target stiffness(NVTS)admittance control strategy is proposed for adaptive force tracking,in which a proportional integral derivative(PID)variable stiffness law is designed to update the stiffness coefficient of the admittance function by the force and position ***,its stability and force-tracking capability are theoretically *** addition,an impact compensator(Impc)is integrated into the NVTS controller to enhance its disturbance-suppression capability when the robot is subjected to strong vibration disturbances in complicated surface polishing *** proposed controllers are validated through four groups of experimental tests using different robots and the corresponding results demonstrate that they have high-accuracy tracking capability and strong adaptability in unknown environments.

关键词： compliant interaction control force tracking PID variable stiffness impact compensator unknown environments

来源：评论

学校读者我要写书评

暂无评论

A Heuristic Informative-Path-Planning Algorithm to Map Unknown Areas and A Benchmark Solution

A Heuristic Informative-Path-Planning Algorithm to Map Unkno...

引用

control Technology and Applications (CCTA),

作者： Mobolaji O. Orisatoki Mahdi Amouzadi Arash M. Dizqah Smart Vehicle Control Laboratory (SVeCLab) School of Engineering and Informatics University of Sussex Brighton UK School of Computing and Mathematical Sciences University of Greenwich Greenwich London UK

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

Informative path planning algorithms are of paramount importance in applications like disaster management to efficiently gather information through a priori unknown environments. This is, however, a complex problem that involves finding a globally optimal path that gathers the maximum amount of information (e.g., the largest map with a minimum travelling distance) while using partial and uncertain local measurements. This paper addresses this problem by proposing a novel heuristic algorithm that continuously estimates the potential mapping gain for different sub-areas across the partially created map, and then uses these estimations to locally navigate the robot. Furthermore, this paper presents a novel algorithm to calculate a benchmark solution, where the map is a priori known to the planar, to evaluate the efficacy of the developed heuristic algorithm over different test scenarios. The findings indicate that the efficiency of the proposed algorithm, measured in terms of the mapped area per unit of travelling distance, ranges from 70% to 80% of the benchmark solution in various test scenarios. In essence, the algorithm demonstrates the capability to generate paths that come close to the globally optimal path provided by the benchmark solution.

关键词： Simultaneous localization and mapping Laser radar Navigation Heuristic algorithms Simulation Measurement uncertainty Estimation

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：