检索结果-内蒙古大学图书馆

Congress on Evolutionary Computation

作者： Peng Wang Bing Xue Jing Liang Mengjie Zhang School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method.

关键词： Dimensionality reduction Limiting Accuracy Genetic programming Clustering algorithms Evolutionary computation Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Redundancy Reduction of Musculoskeletal Model for Robots with Group Sparse Neural Network 6

Redundancy Reduction of Musculoskeletal Model for Robots wit...

引用

6th International Conference on Control and Robotics Engineering, ICCRE 2021

作者： Zhong, Shanlin Zhang, Jinhan Nie, Xiangli Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Science Beijing China CETC Key Laboratory of Data Link Technology Xi'an China

ISBN: (纸本)9780738126128

Creating robots with similar behavior and appearance to humans has been a long-term dream for roboticists. However, the highly redundant characteristic of the muscle system brings tremendous difficulty for the control and manufacture of the musculoskeletal robot. In this paper, we propose a heuristic pruning-based recurrent neural network method with group sparse regularization to reduce redundancy of muscles in musculoskeletal model, which can maintain the motion accuracy of musculoskeletal robots without activating the redundant muscles. Experiments on an upper extremity musculoskeletal model validate the effectiveness of the proposed method. © 2021 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Distributed Optimal Coordination Control for Nonlinear Multi-Agent Systems Under Dos Attack

Distributed Optimal Coordination Control for Nonlinear Multi...

引用

Chinese Control Conference (CCC)

作者： Zhirui Wang Xiaoyuan Zheng Sha Fan School of Artificial Intelligence and Data Science Hebei University of Technology Tianjian P. R. China School of Automation East China University of Science and Technology Shanghai P. R. China

This paper is concerned with the distributed optimal coordination control of nonlinear multi-agent systems (NMASs) under the denial of service (DoS) attacks. The purpose of this paper is to design a distributed control rate for an unknown nonlinear multi-agent system with power interval and time-varying packet loss probability under Dos attack that accurately simulates the impact of DoS attack on NMASs. Then adaptive dynamic programming (ADP) algorithm is used to solve the coupled Hamilton-Jacobi-Bellman (HJB) equation. In order to realize the proposed method, the critic neural network(NN) and actor neural network are used to approximate value functions and control policy. Finally the NN actor-critic policy for optimal control of NMASs under DoS attack with power interval is found. It is proved that the weight estimation error and local neighborhood coordination error are uniformly ultimately bounded (UUB). Finally, the simulation results show the effectiveness of the coordinated control.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Risk estimation in a Markov cost process: lower and upper bounds 24

Risk estimation in a Markov cost process: lower and upper bo...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Gugan Thoppe Prashanth L. A. Sanjay P. Bhat Dept. of Computer Science and Automation Indian Institute of Science (IISc) Bengaluru India and Robert Bosch Centre for Data Science and Artificial Intelligence IIT Madras Chennai India Dept. of Computer Science and Engineering Indian Institute of Technology Madras Chennai India TCS Research Hyderabad India

We tackle the problem of estimating risk measures of the infinite-horizon discounted cost of a Markov cost process. The risk measures we study include variance, Value-at-Risk (VaR), and Conditional Value-at-Risk (CVaR). First, we show that estimating any of these risk measures with ε-accuracy, either in expected or high-probability sense, requires at least Ω(1/ε2) samples. Then, using a truncation scheme, we derive an upper bound for the CVaR and variance estimation. This bound matches our lower bound up to logarithmic factors. Finally, we discuss an extension of our estimation scheme that covers more general risk measures satisfying a certain continuity criterion, such as spectral risk measures and utility-based shortfall risk. To the best of our knowledge, our work is the first to provide lower and upper bounds for estimating any risk measure beyond the mean within a Markovian setting. Our lower bounds also extend to the infinite-horizon discounted costs' mean. Even in that case, our lower bound of Ω(1/ε2) improves upon the existing Ω(1/ε) bound (Metelli et al., 2023).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Causal discovery via conditional independence testing with proxy variables 24

Causal discovery via conditional independence testing with p...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Mingzhou Liu Xinwei Sun Yu Qiao Yizhou Wang School of Computer Science and Center on Frontiers of Computing Studies (CFCS) Peking University Sch. of Data Science Fudan University Dep. of Automation Shanghai Jiao Tong University Center on Frontiers of Computing Studies (CFCS) and School of Computer Science and Inst. for Artificial Intelligence and Nat'l Eng. Research Center of Visual Technology and Nat'l Key Lab of General Artificial Intelligence Peking University

Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias in conditional independence testing commonly employed in constraint-based causal discovery for identifying causal relations. To address this issue, existing methods introduced proxy variables to adjust for the bias caused by unobserveness. However, these methods were either limited to categorical variables or relied on strong parametric assumptions for identification. In this paper, we propose a novel hypothesis-testing procedure that can effectively examine the existence of the causal relationship over continuous variables, without any parametric constraint. Our procedure is based on discretization, which under completeness conditions, is able to asymptotically establish a linear equation whose coefficient vector is identifiable under the causal null hypothesis. Based on this, we introduce our test statistic and demonstrate its asymptotic level and power. We validate the effectiveness of our procedure using both synthetic and real-world data. Code is publicly available at https://***/lmz123321/proxy_causal_discovery.

关键词：

来源：评论

学校读者我要写书评

暂无评论

C2T-HR3D: Cross-Fusion of CNN and Transformer for High-Speed Railway Dropper Defect Detection

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： He, Jin Lv, Fengmao Liu, Jun Wu, Min Chen, Badong Wang, Shiping Chengdu University of Information Technology School of Automation Chengdu611731 China Southwest Jiaotong University School of Computer and Artificial Intelligence Chengdu611756 China A*STAR Fusionopolis 138632 Singapore Xi'an Jiaotong University Xi'an710049 China Fuzhou University College of Computer and Data Science Fuzhou350116 China

The dropper plays a critical role in the overhead contact system (OCS) of high-speed railways, ensuring smooth power transmission and reducing vibration between the contact and messenger wires. However, adverse factors, such as temperature variations, inclement weather, and high-frequency vibrations can lead to dropper loosening and detachment, which deteriorates the collecting current through the pantograph. In severe cases, it can even result in pantograph breakage or contact wire damage, ultimately causing train malfunctions. Unfortunately, existing detection methods fall short in recognizing dropper defects in real-world scenarios. To address this challenge, we propose a novel cross-fusion of convolutional neural network and transformer for high-speed railway dropper defect detection (C2T-HR3D) network. Leveraging a cross-fusion of convolutional neural network (CNN) and transformers, this network accurately recognizes dropper defects in challenging scenarios, such as fog, rain, sun, and night-time conditions. Moreover, it can also accurately identify obscured and small dropper defects from a long distance, significantly improving recall and precision. Extensive experiments have demonstrated that our network outperforms CNN-based, transformer-based, and CNN-transformer state-of-the-art networks by 3.4%, 1.8%, and 2.1%, respectively. The C2T-HR3D network has been successfully deployed on over 300 high-speed trains, detecting more than 10000 dropper defects. © 1963-2012 IEEE.

关键词： Railroads

来源：评论

学校读者我要写书评

暂无评论

SegGPT Meets Co-Saliency Scene

arXiv

引用

arXiv 2023年

作者： Liu, Yi Xu, Shoukun Zhang, Dingwen Han, Jungong School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software Changzhou University Jiangsu Changzhou213000 China The Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230026 China School of Automation Northwestern Polytechnical University Shannxi Xi’an710129 China Department of Computer Science The University of Sheffield United Kingdom

Co-salient object detection targets at detecting coexisted salient objects among a group of images. Recently, a generalist model for segmenting everything in context, called SegGPT, is gaining public attention. In view of its breakthrough for segmentation, we can hardly wait to probe into its contribution to the task of co-salient object detection. In this report, we first design a framework to enable SegGPT for the problem of co-salient object detection. Proceed to the next step, we evaluate the performance of SegGPT on the problem of co-salient object detection on three available datasets. We achieve a finding that co-saliency scenes challenges SegGPT due to context discrepancy within a group of co-saliency images. © 2023, CC BY.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems

arXiv

引用

arXiv 2024年

作者： Yuwono, Steve Schwung, Dorothea Schwung, Andreas Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Lübecker Ring 2 Soest59494 Germany Department of Artificial Intelligence and Data Science in Automation Technology Hochschule Düsseldorf University of Applied Sciences Münsterstraße 156 Düsseldorf40476 Germany

This paper presents a novel transfer learning approach in state-based potential games (TL-SbPGs) for enhancing distributed self-optimization in manufacturing systems. The approach focuses on the practical relevant industrial setting where sharing and transferring gained knowledge among similar-behaved players improves the self-learning mechanism in large-scale systems. With TL-SbPGs, the gained knowledge can be reused by other players to optimize their policies, thereby improving the learning outcomes of the players and accelerating the learning process. To accomplish this goal, we develop transfer learning concepts and similarity criteria for players, which offer two distinct settings: (a) predefined similarities between players and (b) dynamically inferred similarities between players during training. We formally prove the applicability of the SbPG framework in transfer learning. Additionally, we introduce an efficient method to determine the optimal timing and weighting of the transfer learning procedure during the training phase. Through experiments on a laboratory-scale testbed, we demonstrate that TL-SbPGs significantly boost production efficiency while reducing power consumption of the production schedules while also outperforming native SbPGs. © 2024, CC BY-NC-ND.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

The Codecfake dataset and Countermeasures for the Universally Detection of Deepfake Audio

IEEE Transactions on Audio, Speech and Language Processing

引用

IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 386-400页

作者： Yuankun Xie Yi Lu Ruibo Fu Zhengqi Wen Zhiyong Wang Jianhua Tao Xin Qi Xiaopeng Wang Yukun Liu Haonan Cheng Long Ye Yi Sun State Key Laboratory of Media Convergence and Communication Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Department of Automation Tsinghua University Beijing China School of Data Science and Intelligent Media Communication University of China Beijing China School of Cyberspace science and Technology Beijing Institute of Technology Beijing China

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for generalized detection methods. ALM-based deepfake audio currently exhibits widespread, high deception, and type versatility, posing a significant challenge to current audio deepfake detection (ADD) models trained solely on vocoded data. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially constructed the Codecfake dataset, an open-source, large-scale collection comprising over 1 million audio samples in both English and Chinese, focus on ALM-based audio detection. As countermeasure, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original sharpness aware minimization (SAM), we propose the CSAM strategy to learn a domain balanced and generalized minima. In our experiments, we first demonstrate that ADD model training with the Codecfake dataset can effectively detects ALM-based audio. Furthermore, our proposed generalization countermeasure yields the lowest average equal error rate (EER) of 0.616% across all test conditions compared to baseline models. The dataset and associated code are available online.

关键词： Deepfakes Codecs Vocoders Acoustics Training Codes Feature extraction Vector quantization Speech enhancement Speech coding

来源：评论

学校读者我要写书评

暂无评论

The application of artificial intelligence in upper gastrointestinal cancers

引用

Journal of the National Cancer Center 2025年第2期5卷 113-131页

作者： Xiaoying Huang Minghao Qin Mengjie Fang Zipei Wang Chaoen Hu Tongyu Zhao Zhuyuan Qin Haishan Zhu Ling Wu Guowei Yu Francesco De Cobelli Xuebin Xie Diego Palumbo Jie Tian Di Dong CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina University of Science and Technology Beijing BeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang UniversityBeijingChina Key Laboratory of Big Data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijingChina University of Science and Technology of China HefeiChina Beijing Institute of Genomics Chinese Academy of SciencesBeijingChina Beijing University of Chinese Medicine BeijingChina KiangWu Hospital MacaoChina Department of Radiology IRCCS San Raffaele Scientific InstituteMilanItaly

Upper gastrointestinal cancers,mainly comprising esophageal and gastric cancers,are among the most prevalent cancers *** are many new cases of upper gastrointestinal cancers annually,and the survival rate tends to be ***,timely screening,precise diagnosis,appropriate treatment strategies,and effective prognosis are crucial for patients with upper gastrointestinal *** recent years,an increasing number of studies suggest that artificial intelligence(AI)technology can effectively address clinical tasks related to upper gastrointestinal *** studies mainly focus on four aspects:screening,diagnosis,treatment,and *** this review,we focus on the application of AI technology in clinical tasks related to upper gastrointestinal ***,the basic application pipelines of radiomics and deep learning in medical image analysis were ***,we separately reviewed the application of AI technology in the aforementioned aspects for both esophageal and gastric ***,the current limitations and challenges faced in the field of upper gastrointestinal cancers were summarized,and explorations were conducted on the selection of AI algorithms in various scenarios,the popularization of early screening,the clinical applications of AI,and large multimodal models.

关键词： Upper gastrointestinal cancers artificial intelligence Radiomics Esophageal cancer Gastric cancer

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：