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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

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Research on the distributed business data warehouse technology

Research on the distributed business data warehouse technolo...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Cui Zhiming Rui Yannian Liu Wenjie Computer Information Processing Technology Laboratory Suzhou University Suzhou Jiangsu China

The data warehouse is an important technology for modern enterprises as the basis for their decision support systems. This paper introduces a basic method to build a distributed business data warehouse, discusses its functions, and illustrates the design ideas of a distributed business data warehouse. The technologies for a new generation data warehouse are explored.

关键词： Data warehouses Companies Decision making Modems Decision support systems information analysis Distributed databases Embedded software Software tools information processing

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A knowledge sharing and collaboration system model based on Internet

A knowledge sharing and collaboration system model based on ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Wu Gangshan Huang Yuan Shian-Shyong Tseng Zhang Fuyan State Key Laboratory for Novell Software Technology Department of Computer Science and Technology Nanjing University of Technology Jiangsu China Department of Computer and Information Science National Chiao Tung University Hsinchu Taiwan

This paper proposes a knowledge sharing and collaboration system model (InKB) based on the Internet. It gives a way to collaborate and share knowledge between Web-based knowledge systems. InKB model has three layers: data exchange layer, collaboration layer and knowledge-based application layer. The data exchange layer solves the problem of how to represent and manipulate knowledge. The collaboration layer devotes collaboration between servers over the Internet. The knowledge-based application layer defines the user interface for knowledge processing over the Internet, such as information searching, decision-support application and data mining. Data exchange between InKB Webs is in XML format, knowledge can be shared between heterogeneous knowledge bases, and knowledge can be remotely manipulated. Collaboration between InKBs is supported through a collaboration agent, which can find the resource the user wants in the InKB system. As an open system model, InKB can also support HTTP requests such as information retrieval and browsing.

关键词： Collaboration Internet Web server Knowledge based systems information retrieval XML HTML Laboratories computer science information science

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A two-phase feature selection method using both filter and wrapper

A two-phase feature selection method using both filter and w...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huang Yuan Shian-Shyong Tseng Wu Gangshan Zhang Fuyan State Key Laboratory for Novell Software TechnologyDepartment of Computer Science and Technology Nanjing University Nanjing Jiangsu China Department of Computer and Information Science National Chiao Tung University Hsinchu Taiwan

Feature selection is an integral step of the data mining process to find an optimal subset of features. After examining the problems with both the filter and the wrapper approach to feature selection, we propose a two-phase (filter and wrapper) feature selection algorithm that can take advantage of both approaches. It begins by running GFSIC (Genetic Feature Selection with Inconsistency Criterion), a filter approach, to remove irrelevant features, then it runs SBFCV (Sensitivity-Based Feature selection with v-fold Cross-Validation), a wrapper approach, to remove redundant or useless features. Analysis and experimental studies show the effectiveness and scalability of the proposed algorithm. The generalization of the neural network is improved when the algorithm is used to pre-process the training data by eliminating irrelevant and useless features from the neural network's consideration.

关键词： Filters Neural networks Data mining Genetic algorithms Neurons Laboratories computer science information science Algorithm design and analysis Scalability

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Collaborative Fuzzy Linguistic Learning to Low-Resource and Robust Decision System Based on Bounded Rationality

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ACM Transactions on Asian and Low-Resource Language information processing 1000年

作者： Chao Zhang Xiaochuan Li Arun Kumar Sangaiah Wentao Li Baoli Wang Feng Cao Xuekui Shangguan School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Taiwan and Department of Electrical and Computer Engineering Lebanese American University Lebanon College of Artificial Intelligence Southwest University China and School of Computer and Information Technology Shanxi University China School of Mathematics and Information Technology Yuncheng University China Shanxi Information Industry Technology Research Institute Co. Ltd China

Low-resource languages are challenging to process intelligent decision systems due to limited data and resources. As an effective way of processing low-resource languages in intelligent decision systems, fuzzy linguistic approaches excel in transforming original uncertain linguistic information into highly structured data and learning valid decision rules between complex data structures. However, existing fuzzy linguistic methods may not fully capture realistic features of multi-attribute group decision-making (MAGDM), such as incomplete and hesitant linguistic expressions, stable information fusion, and bounded rationality of decision-makers (DMs). Therefore, it is necessary to develop a collaborative fuzzy language learning system based on bounded rationality, low-resource and robust decision-making. Specifically, we present a new multi-granularity (MG) group decision-making (GDM) scheme by using MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) and PT (Prospect Theory) for incomplete hesitant fuzzy linguistic information systems (I-HFL-ISs), where MG GDM aims to discover knowledge from complex MAGDM problems with MG features. To achieve the above goal, we first introduce the concept of MG-I-HFL-ISs to represent incomplete, hesitant and imprecise linguistic evaluation information offered by multiple decision-makers (DMs). Then, we apply a valid transformation scheme to convert MG-I-HFL-ISs into MG-HFL-ISs, and use the MG probability rough set (PRS) to develop a series of MG-HFL-PRSs with the support of MULTIMOORA. Afterwards, an HFL MG GDM method is designed by integrating MULTIMOORA and PT for solving MAGDM problems with MG-I-HFL-ISs. The proposed method can effectively synthesize low-resource languages and mine useful decision-making knowledge. At last, a drug selection case and a simulated case are performed for showing the rationality of the designed HFL MG GDM scheme.

关键词： Fuzzy linguistic approach collaborative multiple feature fusion low-resource languages granular computing decision system

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Cost-Efficient Deep Neural Network Placement in Edge Intelligence-Enabled Internet of Things

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ACM Transactions on Sensor Networks 1000年

作者： Hao Tian Xiaolong Xu Hongyue Wu Qingzhan Zhao Jianguo Dai Maqbool Khan State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Nanjing China School of Software Nanjing University of Information Science and Technology Nanjing Jiangsu China Nanjing China College of Intelligence and Computing Tianjin University Tianjin China Tianjin China Geospatial Information Engineering Research Center College of Information Science and Technology Shihezi University Shihezi Xinjiang China Shihezi China Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur Pakistan Haripur Pakistan

Edge intelligence (EI) integrates edge computing and artificial intelligence empowering service providers to deploy deep neural networks (DNNs) on edge servers in proximity to users to provision intelligent applications (e.g., autonomous driving) for ubiquitous Internet of Things (IoT) in smart cities, which facilitates the quality of experience (QoE) of users and improves the processing and energy efficiency. However, considering DNN is typically computational-intensive and resource-hungry, conventional placement approaches ignore the influence of multi-dimensional resource requirements (processor, memory, etc.), which may degrade the real-time performance. Moreover, with the increasing scale of geo-distributed edge servers, centralized decision-making is still challenging to find the optimal strategies effectively. To overcome these shortcomings, in this paper we propose a game theoretic DNN placement approach in EI-enabled IoT. First, a DNN placement optimization problem is formulated to maximize system benefits, which is proven to be \(\mathcal {N}\mathcal {P}\)-hard and model the original problem as an exact potential game (EPG). Moreover, an EPG-based DNN model placement algorithm, named EPOL, is designed for edge servers to make sub-optimal strategies independently and theoretical analysis is possessed to guarantee the performance of EPOL. Finally, real-world dataset based experimental results corroborate the superiority and effectiveness of EPOL.

关键词： Edge intelligence DNN placement Game theory Internet of Things

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Deep Stable Learning for Cross-lingual Dependency Parsing

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ACM Transactions on Asian and Low-Resource Language information processing 1000年

作者： Haijiang Liu Chen Qiu Qiyuan Li Maofu Liu Li Wang Jinguang Gu Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Institute of Scientific and Technical Information of China Beijing China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing China

The Cross-lingual Dependency Parsing (XDP) task poses a significant challenge due to the differences in dependency structures between training and testing languages, known as the out-of-distribution (OOD) problem. Our research delved into this issue in the XDP dataset by selecting 43 languages from 22 language families. We found that the primary factor of the OOD problem is the unbalanced length distribution among languages. To address the impact of the OOD problem, we propose deep stable learning for Cross-lingual Dependency Parsing (SL-XDP), which utilizes deep stable learning with a feature fusion module. In detail, we implemented five feature fusion operations for generating comprehensive representations with dependency relations and the deep stable learning algorithm to decorrelate dependency structures with sequence length. Our experiments on Universal Dependencies have demonstrated that SL-XDP can lessen the impact of the OOD problem and improve the model generalization among 21 languages, with a maximum improvement of 18%.

关键词： Cross-lingual Dependency Parsing Deep Stable Learning Out-of-Distribution Problem

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

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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

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

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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

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Efficient Low-Resource Medical information processing Based on Semantic Analysis and Granular Computing

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ACM Transactions on Asian and Low-Resource Language information processing 1000年

作者： Yuting Cheng Chao Zhang Arun Kumar Sangaiah Xueqing Fan Anna Wang Liyin Wang Yixin Liu School of Computer and Information Technology Shanxi University China School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Taiwan and Department of Electrical and Computer Engineering Lebanese American University Lebanon Chumin College Shanxi University China

The rise of the digital economy and e-commerce has fostered a movement towards efficient low-resource medical information processing, a trend that holds great importance in the healthcare sector. Diabetes, being a widespread chronic condition, has witnessed the introduction of glucometers, which offer patients a convenient method of monitoring their blood sugar levels. However, it is worth noting that a considerable proportion of online comments may be subject to emotional bias or contain inaccurate information. Furthermore, the performance of glucometers can be influenced by several attributes, including price, accuracy and portability, thereby potentially complicating the decision-making process for consumers. Semantic analysis can be employed to acquire valuable information, aiding consumers in reasonably choosing the suitable glucometer. This paper utilizes the benefits of granular computing, an emerging computing paradigm, to effectively handle incomplete and uncertain medical information. It employs generalized fuzzy sets, rough sets and three-way decisions (TWD) techniques to boost the accuracy and reliability of medical information fusion. Subsequently, the MABAC (Multi-Attribute Border Approximation Area Comparison) method is utilized to evaluate the reviews of every glucometer, calculate their aggregated scores, and rank and compare them. Ultimately, in light of consumers’ needs and trade-offs, the glucometer with the highest score can be selected. The proposed approach comprehensively considers the weight and priority of multiple attributes, reduces information overload and mitigates selection difficulties, thereby enhancing the accuracy and reliability of low-resource medical information processing.

关键词： Semantic analysis granular computing medical information processing three-way decision

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