检索结果-内蒙古大学图书馆

Advances in simulated modeling of vibration systems based on computational intelligence

自然科学进展(英文版) 2002年第12期12卷 881-886页

作者： LIANG Yanchun LEE Heow Pueh LIN Wuzhong LIM Siak Piang LEE Kwok Hong Zhou Chunguang College of Computer Science and Technology Jilin University Changchun 130012 China Institute of High Performance Computing Singapore 117528 Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sci Institute of High Performance Computing Singapore 117528 Department of Mechanical Engineering National University of Singapore Singapore 119260 Singapore College of Computer Science and Technology Jilin University Changchun 130012 China

Computational intelligence is the computational simulation of the bio-intelligence, which includes artificial neural networks, fuzzy systems and evolutionary computations. This article summarizes the state of the art in the field of simulated modeling of vibration systems using methods of computational intelligence, based on some relevant subjects and the authors' own research work. First, contributions to the applications of computational intelligence to the identification of nonlinear characteristics of packaging are reviewed. Subsequently, applications of the newly developed training algorithms for feedforward neural networks to the identification of restoring forces in multi-degree-of-freedom nonlinear systems are discussed. Finally, the neural-network-based method of model reduction for the dynamic simulation of microelectromechanical systems (MEMS) using generalized Hebbian algorithm (GHA) and robust GHA is outlined. The prospects of the simulated modeling of vibration systems using techniques of computational intelligence are also indicated.

关键词： computational intelligence artificial neural network fuzzy system evolutionary computation vibration system

来源：评论

学校读者我要写书评

暂无评论

The classification method based on hyper surface

The classification method based on hyper surface

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Qing He Zhong-Zhi Shi Li-An Ren The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Graduate College of University of Science and Technology of China Beijing China

The main idea of SVM, i.e. Support Vector Machine, is mapping nonlinear separable data into higher dimension linear space where the data can be separated by hyper plane. Based on Jordan Curve Theorem, a general classification method HSC, Classification based on Hyper Surface, is put forward in this paper. The separating hyper surface is directly made to classify large database. The data are classified according to whether the intersecting number is odd or even. It is a novel approach which has no need of either mapping from lower dimension space to higher dimension space or considering kernel function. It can directly solve the nonlinear classification problem. The experiments show that the new method can efficiently and accurately classify large data.

关键词： Support vector machines Support vector machine classification Machine learning Machine learning algorithms Space technology Databases Artificial intelligence Neural networks Helium Laboratories

来源：评论

学校读者我要写书评

暂无评论

Autonomous mental development for agent

Autonomous mental development for agent

引用

International Conferences on Info-tech and Info-net (ICII)

作者： Jinhua Zheng Zhongzhi Shi Laboratory of Intelligent Information ProcessingInstitute of Computing Technology Chinese Academy and Sciences Beijing China Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Human being have capabilities to learn after birth. In analogy, it seems that the machine can be let its "brain" or mental development system to learn new tasks without the need for programming. In this paper, we discuss an automated mental development in the multi agent environment (MAGE), including the framework, model and future work. The goal is that the agent can autonomously learn like a human and various knowledge can be memorised in a relevant form autonomously.

关键词： Autonomous mental development Humans Learning systems Robot sensing systems intelligent robots intelligent sensors Machine learning Artificial intelligence Robotics and automation Programming profession

来源：评论

学校读者我要写书评

暂无评论

GAHE: Geometry-Aware Embedding for Hyper-Relational Knowledge Graph Representation

引用

ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Zongsheng Cao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security SKLOIS) Institute of Information Engineering Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Security Department of Alibaba Group China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University China

Knowledge graphs have proven highly effective for learning representations of entities and relations, with hyper-relational knowledge graphs (HKGs) gaining increased attention due to their enhanced representation capabilities. Each fact in an HKG consists of a main triple supplemented by attribute-value qualifiers that provide additional contextual information. Due to the complexity of hyper-relations, HKGs typically contain complex geometric structures, such as hierarchical, ring, and chain structures, often mixed together. However, previous work mainly embeds HKGs into Euclidean space, limiting their ability to capture these complex geometric structures simultaneously. To address this challenge, we propose a novel model called Geometry Aware Hyper-relational Embedding (GAHE). Specifically, GAHE adopts a multi-curvature geometry-aware approach by modeling HKGs in Euclidean space (zero curvature), hyperbolic space (negative curvature), and hyperspherical space (positive curvature) in a unified framework. In this way, it can integrate space-invariant and space-specific features to accurately capture the diverse structures in HKGs. In addition, GAHE introduces a module termed hyper-relational subspace learning, which allocates multiple sub-relations for each hyper-relation. It enables the exploitation of abundant latent semantic interactions and facilitates the exploration of fine-grained semantics between attribute-value pairs and hyper-relations across multiple subspaces. Furthermore, we provide theoretical guarantees that GAHE is fully expressive and capable of modeling a wide range of semantic patterns for hyper-relations. Empirical evaluations demonstrate that GAHE achieves state-of-the-art results on both hyper-relational and binary-relational benchmarks.

关键词： Knowledge Graph Hyper-relational Learning Multicurvature Spaces Geometry Aware

来源：评论

学校读者我要写书评

暂无评论

The Semantic Web – ASWC 2006 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Riichiro Mizoguchi Zhongzhi Shi Fausto Giunchiglia

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent Reinforcement Learning based Edge Content Caching for Connected Autonomous Vehicles in IoV

引用

ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaolong Xu Linjie Gu Muhammad Bilal Maqbool Khan Yiping Wen Guoqiang Liu Yuan Yuan School of Software Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing University of Information Science and Technology China Changwang School of Honors Nanjing University of Information Science and Technology China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Korea Department of IT and Computer Science Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Pakistan School of Computer Science and Engineering Hunan University of Science and Technology China School of Software Nanjing University of Information Science and Technology China School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Zhongguancun Laboratory China

Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.

关键词： Content Caching Reinforcement Learning Connected Autonomous Driving

来源：评论

学校读者我要写书评

暂无评论

End-to-End Explainable Fake News Detection Via Evidence-Claim Variational Causal Inference

引用

ACM Transactions on information Systems 1000年

作者： Jinguang Wang Shengsheng Qian Jun Hu Wenxiang Dong Xudong Huang Richang Hong School of Public Security and Emergency Management Anhui University of Science and Technology China and State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining Anhui University of Science and Technology China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China and School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computing National University of Singapore Singapore Institute of Dataspace Hefei Comprehensive National Science Center China School of Computer Science and Information Engineering Hefei University of Technology China

Explainable Fake News Detection (EFND) is a new challenge that aims to verify news authenticity and provide clear explanations for its decisions. Traditional EFND methods often treat the tasks of classification and explanation as separate, ignoring the fact that explanation content can assist in enhancing fake news detection. To overcome this gap, we present a new solution: the End-to-end Explainable Fake News Detection Network (\(EExpFND\)). Our model includes an evidence-claim variational causal inference component, which not only utilizes explanation content to improve fake news detection but also employs a variational approach to address the distributional bias between the ground truth explanation in the training set and the prediction explanation in the test set. Additionally, we incorporate a masked attention network to detail the nuanced relationships between evidence and claims. Our comprehensive tests across two public datasets show that \(EExpFND\) sets a new benchmark in performance. The code is available at https://***/r/EExpFND-F5C6.

关键词： Social media Explainable fake news detection Causal inference

来源：评论

学校读者我要写书评

暂无评论

Vehicle Dynamics and Interaction for Trajectory Prediction and Traffic Control

引用

ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Jian Chen Shaorui Zhou Wei Wang Yuzhu Hu Jianqing Li Ben-Guo He Junxin Chen Marwan Omar Ali Kashif Bashir Xiping Hu School of Intelligent Systems Engineering Sun Yat-Sen University China Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University China the School of Software Dalian University of Technology China Information Technology and Management Illinois Institute of Technology USA Department of Computing and Mathematics Manchester Metropolitan University UK Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.

关键词： Trajectory Prediction Vehicle Dynamics Interaction Perception Graph Convolution Attention Mechanism

来源：评论

学校读者我要写书评

暂无评论

Agent computing and Multi-Agent Systems 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Zhong-Zhi Shi Ramakoti Sadananda

ISBN: (数字)9783540368601

ISBN: (纸本)9783540367079

PRIMA is a series of workshops on agent computing and multi-agent systems, integrating the activities in Asia and Pacific Rim countries. Agent computing and multi-agent systems are computational systems in which several autonomous or se- autonomous agents interact with each other or work together to perform some set of tasks or satisfy some set of goals. These systems may involve computational agents that are homogeneous or heterogeneous, they may involve activities on the part of agents having common or distinct goals, and they may involve participation on the part of humans and intelligent agents. The aim of PRIMA 2006 was to bring together Asian and Pacific Rim researchers and developers from academia and industry to report on the latest technical advances or domain applications and to discuss and explore scientific and practical problems as raised by the participants. PRIMA 2006 received 203 submitted papers. Each paper was reviewed by two internationally renowned Program Committee members. After careful reviews, 39 regular papers and 57 short papers were selected for this volume. We would like to thank all the authors who submitted papers to the workshop. We are very grateful to all Program Committee members and reviewers for their splendid work in reviewing the papers. Finally, we thank the editorial staff of Springer for publishing this volume in the Lecture Notes in Artificial Intelligence series.

关键词： Artificial Intelligence Computer Communication Networks Software Engineering Logics and Meanings of Programs

来源：评论

学校读者我要写书评

暂无评论

Distributed Computation Offloading and Power Control for UAV-Enabled Internet of Medical Things

引用

ACM Transactions on Internet technology 1000年

作者： Jiakun Gao Xiaolong Xu Lianyong Qi Wanchun Dou Xiaoyu Xia Xiaokang Zhou School of Software Nanjing University of Information Science and Technology NanJing China School of Software Nanjing University of Information Science and Technology NanJing China and Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science and Technology Nanjing China College of Computer Science and Technology China University of Petroleum (East China) QingDao China and State Key Laboratory for Novel Software Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Technology RMIT University Victoria Australia Faculty of Data Science Shiga University Hikone Japan

The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\%\) reduction in energy consumption and \(16\%\) decrease in average delay.

关键词： Internet of Medical Things (IoMT) Mobile Edge computing (MEC) Unmanned Aerial Vehicles (UAV) Non-Orthogonal Multiple Access (NOMA)

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：