检索结果-内蒙古大学图书馆

Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition

INFORMATION FUSION 2022年 80卷 1-22页

作者： Zheng, Wenbo Yan, Lan Gou, Chao Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China

With the rapid growth of the Internet of Things (IoT), smart systems and applications are equipped with an increasing number of wearable sensors and mobile devices. These sensors are used not only to collect data but, more importantly, to assist in tracking and analyzing the daily human activities. Sensor-based human activity recognition is a hotspot and starts to employ deep learning approaches to supersede traditional shallow learning that rely on hand-crafted features. Although many successful methods have been proposed, there are three challenges to overcome: (1) deep model's performance overly depends on the data size;(2) deep model cannot explicitly capture abundant sample distribution characteristics;(3) deep model cannot jointly consider sample features, sample distribution characteristics, and the relationship between the two. To address these issues, we propose a meta-learning-based graph prototypical model with priority attention mechanism for sensor-based human activity recognition. This approach learns not only sample features and sample distribution characteristics via meta-learning-based graph prototypical model, but also the embeddings derived from priority attention mechanism that mines and utilizes relations between sample features and sample distribution characteristics. What is more, the knowledge learned through our approach can be seen as a priori applicable to improve the performance for other general reasoning tasks. Experimental results on fourteen datasets demonstrate that the proposed approach significantly outperforms other state-of-the-art methods. On the other hand, experiments of applying our model to two other tasks show that our model effectively supports other recognition tasks related to human activity and improves performance on the datasets of these tasks.

关键词： Meta-learning Internet of Things Graph model Attention mechanisms

来源：评论

学校读者我要写书评

暂无评论

Perception and Planning of Intelligent Vehicles Based on BEV in Extreme Off-Road Scenarios

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第4期9卷 4568-4572页

作者： Fan, Jingjing Fan, Lili Ni, Qinghua Wang, Junhao Liu, Yi Li, Ren Wang, Yutong Wang, Sanjin Jiangsu Ind Innovat Ctr Intelligent Equipment Co Liyang 213300 Peoples R China North China Univ Technol Sch Elect & Control Engn Beijing 100144 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Dalian Minzu Univ Sch Sci Dalian 116600 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Anhui Ankai Automobile Co Ltd Hefei 230000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China China North Ind North Automat Control Technol Re North Automat Control Technol Inst Taiyuan 030006 Peoples R China

In extreme off-road scenarios, autonomous driving technology holds strategic significance for enhancing emergency rescue capabilities, reducing labor intensity, and mitigating safety risks. Challenges such as adverse conditions, complex terrains, unstable satellite signals, and lack of roads pose serious safety challenges for autonomous driving. This perspective first delves into a Bird's Eye View (BEV)-based perception-planning framework, aiming to enhance the adaptability of intelligent vehicles to their environment. Subsequently, this perspective further discusses key issues such as Cyber-Physical-Social systems (CPSS), foundation models, and technologies like Sora for off-road scenario generation, vehicle cognitive enhancement, and autonomous decision-making. Ultimately, the discussed framework is poised to endow intelligent vehicles with the capability to perform challenging tasks in complex off-road scenarios, realizing a more efficient, safer, and sustainable transportation system, thereby providing better support for the low-altitude economy.

关键词： Feature extraction Radar Roads Trajectory Millimeter wave communication Point cloud compression Image color analysis Cyber-Physical-Social systems (CPSS) emergency management intelligent vehicles low-altitude economy off-road scenarios

来源：评论

学校读者我要写书评

暂无评论

Sora for Hierarchical Parallel Motion Planner: A Safe End-to-End Method Against OOD Events

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第4期9卷 4573-4576页

作者： Teng, Siyu Yan, Ran Zhang, Xiaotong Li, Yuchen Wang, Xingxia Wang, Yutong Tian, Yonglin Yu, Hui Li, Lingxi Chen, Long Wang, Fei-Yue Nanyang Technol Univ Singapore 639798 Singapore Hong Kong Baptist Univ Hong Kong Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100045 Peoples R China Hong Kong Baptist Univ Hong Kong Peoples R China WAYTOUS Inc Beijing 100083 Peoples R China Univ Portsmouth Sch Creat Technol Portsmouth PO1 2UP England Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA WAYTOUS Inc Beijing 100083 Peoples R China

End-to-end motion planners have shown great potential for enabling fully autonomous driving. However, when facing out-of-distribution (OOD) events, these planners might not guarantee the optimal prediction of control commands. To better enhance safety, an end-to-end method that benefits robust and general policy learning from potential OOD events is urgently desirable. In this perspective, Sore4PMP, a hierarchical parallel motion planner, is presented as a suitable solution. Based on raw perception data and descriptive prompts, Sore4PMP can first leverage the advanced generative capabilities of Sora to generate virtual OOD events, and then integrate these events into the decision-making process, thereby enhancing the robustness and generalization of autonomous vehicles (AVs) in emergency scenarios. With a comprehensive outlook, this perspective aims to provide a potential direction for the development of foundation models coupled with autonomous driving and finally promote the safety, efficiency, reliability, and sustainability of AVs.

关键词： Planning Feature extraction Robustness Vectors Visualization Task analysis Cognition Sora end-to-end planning safety motion planning interpretable imitation learning

来源：评论

学校读者我要写书评

暂无评论

A Multi-Modal Edge Consistency Metric Based on Regression Robustness of Truncated SVD

引用

IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 1065-1069页

作者： Zhu, Mingzhu Yu, Junzhi Gao, Zhang He, Bingwei Liu, Jiantao Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Robot & Intelligent Syst Shenzhen 518055 Peoples R China Peking Univ State Key Lab Turbulence & Complex Syst Dept Adv Mfg & Robot BIC ESATColl Engn Beijing 100871 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, we propose a novel edge consistency metric for multi-modal correspondence. It is based on a novel observation on image truncated SVD (singular value decomposition) termed regression robustness, which describes the fact that, a good approximation from image truncated SVD can be inherited even if the eigen-images change due to expansion and channel-dependent offsets. Compared to state-of-the-arts, multi-modal edge consistency metric can simultaneously handle multiple images with complex modality changes, including local variation, gradient reverse, intensity order change, and texture loss. Its complexity is almost linear to pixel number. Remarkable accuracies have been achieved in experiments.

关键词： Image edge detection Measurement Robustness Linear regression complexity theory Noise measurement Mutual information Regression robustness multi-modal corres-pondence edge consistency singular value decomposition

来源：评论

学校读者我要写书评

暂无评论

On Iterative Proportional Updating: Limitations and Improvements for General Population Synthesis

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第3期52卷 1726-1735页

作者： Ye, Peijun Tian, Bin Lv, Yisheng Li, Qijie Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Univ Technol Sch Artificial Intelligence & Automat Beijing 100124 Peoples R China Qingdao Acad Intelligent Ind Parallel Intelligence Res Ctr Qingdao 266109 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China

Population synthesis is the foundation of the agent-based social simulation. Current approaches mostly consider basic population and households, rather than other social organizations. This article starts with a theoretical analysis of the iterative proportional updating (IPU) algorithm, a representative method in this field, and then gives an extension to consider more social organization types. The IPU method, for the first time, proves to be unable to converge to an optimal population distribution that simultaneously satisfies the constraints from individual and household levels. It is further improved to a bilevel optimization, which can solve such a problem and include more than one type of social organization. Numerical simulations, as well as population synthesis using actual Chinese nationwide census data, support our theoretical conclusions and indicate that our proposed bilevel optimization can both synthesize more social organization types and get more accurate results.

关键词： Sociology Statistics Optimization Organizations Automation Iterative algorithms Agent-based simulation bilevel optimization iterative proportional updating (IPU) population synthesis

来源：评论

学校读者我要写书评

暂无评论

Observer-based event and self-triggered adaptive output feedback control of robotic manipulators

引用

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2022年第16期32卷 8842-8873页

作者： Gao, Jie He, Wei Qiao, Hong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing Key Lab Res & Applicat Robot Intelligence Beijing Peoples R China Univ Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai Peoples R China

This article investigates the event and self-triggered adaptive output feedback control of a manipulator suffering from limited knowledge of states and dynamics, to realize the trajectory tracking with less communication occupation. In this control scheme, the configuration of co-located observer and controller with discontinued output feedback is considered. To guarantee the convergence of observation and control errors with few events as much as possible, an adaptive event-triggered mechanism based on model estimation is constructed to compensate for the error accumulation produced by the intermittent open-loop. Based on the model state, adaptive backstepping method with network estimation is used for deriving the controller, to solve the control stability under uncertainty of system dynamics. Aiming at removing the "derivative explosion and singularity" of discontinuous virtual signal, a first-order filter is incorporated to get the smooth approximation of the virtual signal, and an additional self-adaption signal is designed for the filtering error compensation. In view of the state updating at event instants, a gradual updating method is designed such that the state jumping-induced chattering instability could be handled. With the above designed method, a dead-zone event-triggered condition with the relative threshold and variable tolerance boundary is built to avoid Zeno-behavior. Furthermore, an easy-implemented self-triggered mechanism is also constructed. Finally, the Lyapunov function is utilized to derive the setting principle for the stability of the system, and the simulation is given to show the validity of the proposed control method.

关键词： first-order filter impulsive dynamical system model-based event-triggered control neural network nonlinear uncertainty observer estimation robotic manipulator

来源：评论

学校读者我要写书评

暂无评论

An ACP-Based Parallel Approach for Color Image Encryption Using Redundant Blocks

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13181-13196页

作者： Zheng, Wenbo Yan, Lan Gou, Chao Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China

Public concerns on image encryption grow significantly as the development and application of edge computing and the Internet of Things intensified recently. However, most existing image cryptosystems are not sophisticated enough to resist the two major attack strategies available currently, that is: 1) differential attacks and 2) chosen-plaintext attacks, which are famous for their destructive power, especially their capability of exploiting cryptosystems' features to recover the secret key. In this article, we propose an artificial image, computational experiment, and parallel execution (ACP)-based color image encryption approach using redundant blocks. First, a redundant blocks strategy with redundant spaces is proposed to prevent differential attacks and accelerate operating speed while guaranteeing the security of image cryptosystems. Second, real-world chaotic data (e.g., stock data) are obtained to generate artificial images and conduct computational experiments. Furthermore, artificial images are encrypted via real-world chaos, while the original images are encrypted via simulated chaos (such as Chen's hyperchaos). Finally, we design the process of parallel execution for image encryption and use DNA XOR to merge two groups of encrypted subimages to fuse the effect of the chaotic characteristics in both the real world and the simulation. The final encrypted image is realized through the recovery of redundant blocks. The ACP mechanism of color image encryption achieves the goal of improving the sophistication of chaos-based cryptosystems and resists both the differential and chosen-plaintext attacks. Experimental results and security analysis show that our approach provides not only excellent encryption but also security sufficient to prevent known attacks.

关键词： Encryption DNA Color Resists Computational modeling Image coding complex systems Artificial image and parallel execution (ACP) method color image encryption computational experiment image division block parallel computing

来源：评论

学校读者我要写书评

暂无评论

Improving Domain-Adaptive Person Re-Identification by Dual-Alignment Learning With Camera-Aware Image Generation

引用

IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2021年第11期31卷 4334-4346页

作者： Zhang, Chenyang Tang, Yongqiang Zhang, Zhizhong Li, Ding Yang, Xuebing Zhang, Wensheng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing 100190 Peoples R China

Domain adaptation in person re-identification (re-ID) has always been challenging, especially for the lack of supervision information on the target domain. Existing methods generally introduced extra supervision by adversarial learning techniques, then added all the augmented data in the training process to optimize the re-ID model. However, the direct utilization of all the generated data not only increases additional computational cost but also ignores the potential correlation between the origin and generated data. In this article, we propose a novel dual-alignment learning framework (DAL) with camera-aware image generation to efficiently and effectively tackle this issue. Specifically, we propose a camera transfer matching module to generate additional training images with different camera styles, and construct the matching pairs with each containing a origin image and one corresponding camera transferred image. To strengthen the correlation of images for each matching pair, we align the pseudo-labels via clustering algorithm to reduce the pseudo-labels distribution discrepancy between the origin and generated images. Besides, to avoid model degeneration affected by some inaccurate pseudo-labels on unlabelled data, we maximize the mutual information to align the image feature representations of matching pair. The DAL allows us to decrease the camera variance and enhance the discrimination ability of re-ID model. Extensive experiments on three large-scale benchmarks demonstrate the superiority of DAL over state-of-the-art methods.

关键词： Cameras Training Data models Correlation Clustering algorithms Adaptation models Prediction algorithms Person re-identification convolutional neural networks generative adversarial networks mutual information

来源：评论

学校读者我要写书评

暂无评论

ReCUS: Reconvolution and Upsampling Network for Object Detection 7

ReCUS: Reconvolution and Upsampling Network for Object Detec...

引用

7th IEEE International Conference on Cloud Computing and Intelligence systems, CCIS 2021

作者： Li, Fudong Gao, Dongyang Yang, Yuequan Cao, Zhiqiang Wang, Wei Yangzhou225000 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Affiliated Hospital of Yangzhou University Yangzhou225012 China

ISBN: (纸本)9781665441490

Most of the mainstream object detection models such as RetinaNet, SSD, YOLO, and Faster RCNN hardly achieve a good balance between detection accuracy and speed. A major reason is rich deep feature semantic information of images is not fully exploited. To solve this problem, a novel deep convolutional network structure termed as reconvolution and upsampling network (ReCUS) is proposed. In the ReCUS, a modified path aggregation network(mPAN) is added after the backbone, which is beneficial to strengthen the foreground salient feature information and weaken background information. Moreover, two new spatial pyramid pooling (SPP) modules are embedded before output heads for multi-scale fusion of local and global features. The experiments show that the effectiveness of our proposed ReCUS. Furthermore, the better detectability of the ReCUS network is demonstrated for both small scale objects and large scale objects. © 2021 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

AcWRKY28 mediated activation of AcCPK genes confers salt tolerance in pineapple(Ananas comosus)

引用

Horticultural Plant Journal 2024年第2期10卷 398-412页

作者： Qiao Zhou Samaranayaka Vidana Gamage Nirosha Priyadarshani Rongjuan Qin Han Cheng Tiantian Luo Myat Hnin Wai Mohammad Aqa Mohammadi Yanhui Liu Chang Liu Hanyang Cai Xiaomei Wang Yeqiang Liu Yuan Qin Lulu Wang Guangxi Key Lab of Sugarcane Biology State Key Laboratory for Conservation and Utilization of Subtropical Agro-BioresourcesCollege of AgricultureGuangxi UniversityNanningGuangxi 530004China State Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops College of Life ScienceCollege of Plant ProtectionCollege of AgricultureCollege of HorticultureFujian Provincial Key Laboratory of Haixia Applied Plant Systems BiologyCenter for Genomics and BiotechnologyFujian Agriculture and Forestry UniversityFuzhouFujian 350002China Pingtan Science and Technology Research Institute of Fujian Agriculture and Forestry University PingtanFujian 350400China National Education Commission BMICHColombo00700Sri Lanka Fishery Multiplication Management Station of Lijiang River Water Supply Hub Project GuilinGuangxi 541001China Horticulture Research Institute Guangxi Academy of Agricultural SciencesNanning Investigation Station of South Subtropical Fruit TreesMinistry of AgricultureNanningGuangxi 530007China

Unfavorable environmental cues severely affect crop productivity resulting in significant economic losses to farmers. In plants, multiple regulatory genes, such as the WRKY transcription factor (TF) family, modulate the expression of defense genes. However, the role of the pineapple WRKY genes is poorly understood. Here, we studied the pineapple WRKY gene, AcWRKY28, by generating AcWRKY28 over-expressing transgenic pineapple plants. Overexpression of AcWRKY28 enhanced the salt stress resistance in transgenic pineapple lines. Comparative transcriptome analysis of transgenic and wild-type pineapple plants showed that “plant-pathogen interaction” pathway genes, including 9calcium-dependent protein kinases (CPKs), were up-regulated in AcWRKY28 over-expressing plants. Furthermore, chromatin immunoprecipitation and yeast one-hybrid assays revealed AcCPK12, AcCPK3, AcCPK8, AcCPK1, and AcCPK15 as direct targets of AcWRKY28. Consistently, the study of AcCPK12 over-expressing Arabidopsis lines showed that AcCPK12 enhances salt, drought, and disease resistance. This study shows that AcWRKY28 plays a crucial role in promoting salt stress resistance by activating the expression of AcCPK genes.

关键词： Pineapple AcWRKY AcCPK Transcription factor Salt stress ChIP

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：