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Decoding OvTDM with sphere-decoding algorithm

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The Journal of China Universities of Posts and Telecommunications 2008年第4期15卷 35-39页

作者： JIN Xin LI Dao-ben key laboratory of information processing and intelligent Technology Beijing University of Posts and Telecommunications Beijing 100876 China

Overlapped time division multiplexing （OvTDM） is a new type of transmission scheme with high spectrum efficiency and low threshold signal-to-noise ratio （SNR）. In this article, the structure of OvTDM is introduced and the sphere-decoding algorithm of complex domain is proposed for OvTDM. Simulations demonstrate that the proposed algorithm can achieve maximum likelihood （ML） decoding with lower complexity as compared to traditional maximum likelihood sequence demodulation （MLSD） or viterbi algorithm （VA）.

关键词： OvTDM MLSD sphere-decoding

Online Fault-Tolerant Tracking Control With Adaptive Critic for Nonaffine Nonlinear Systems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期 215-227页

作者： Ding Wang Lingzhi Hu Xiaoli Li Junfei Qiao IEEE the School of information Science and Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protection and Beijing Institute of Artificial Intelligence Beijing University of Technology

In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator ***, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network(NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control,respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov *** tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.

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Non-fragile Observer Design for Fractional-order One-sided Lipschitz Nonlinear Systems

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International Journal of Automation and computing 2013年第4期10卷 296-302页

作者： Yong-Hong Lan Wen-Jie Li Yan Zhou Yi-Ping Luo School of information Engineering Xiangtan University key laboratory of intelligent Computing and information processing of Ministry of Education Xiangtan University Institute of intelligent Control Hunan Institute of Engineering

This paper is concerned with the problem of the full-order observer design for a class of fractional-order Lipschitz nonlinear systems. By introducing a continuous frequency distributed equivalent model and using an indirect Lyapunov approach, the sufficient condition for asymptotic stability of the full-order observer error dynamic system is presented. The stability condition is obtained in terms of LMI, which is less conservative than the existing one. A numerical example demonstrates the validity of this approach.

关键词： Fractional-order nonlinear system observer design indirect Lyapunov approach linear matrix inequality (LMI).

HSNR: A Network Representation Learning Algorithm Using Hierarchical Structure Embedding

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Chinese Journal of Electronics 2020年第6期29卷 1141-1152页

作者： YE Zhonglin ZHAO Haixing ZHU Yu XIAO Yuzhi College of Computer Qinghai Normal University Tibetan information processing and Machine Translation key laboratory of Qinghai Province key laboratory of Tibetan information processing Ministry of Education

Vertices in the same group tend to connect densely,and usually share common *** of different sizes reflect the relations of vertices in different ranges,and also reflect the features of different orders of the *** this work,we propose a novel network representation learning algorithm by introducing group features of vertices of different orders to learn more discriminative network representations,named as network representation learning algorithm using Hierarchical structure embedding(HSNR).HSNR algorithm firstly constructs hierarchical relations of network structures of different orders based on greedy algorithm and *** order to introduce hierarchical features into the network representation learning model,HSNR algorithm then introduces the idea of multi-relational modeling from knowledge representation,and converts the hierarchical relations into the triplet form between ***,HSNR proposes a joint learning model embedding vertex triplets into the network *** experimental results show that the HSRN algorithm presented has an excellent performance in network vertex classification task on three real-world datasets.

关键词： graph theory greedy algorithms knowledge representation learning (artificial intelligence) network vertex classification task HSRN algorithm joint learning model knowledge representation network representation learning model hierarchical features greedy algorithm network structures hierarchical relations HSNR algorithm discriminative network representations group features novel network representation vertices Hierarchical structure embedding network representation learning algorithm Network representation learning Network embedding Network vertex embedding Network data mining Network feature encoding

Designing a Top—Level Ontology of Human Beings：A Multi—Perspective Approach

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Journal of Computer Science & Technology 2002年第5期17卷 636-656页

作者：田雯顾芳曹存根 key laboratory of intelligent information processing Institute of Computing Technology The Chinese Academy of Sciences Beijing 100080 P.R. China

Knowledge about human beings is an integral part of any intelligent agent ofconsiderable significance. Delimiting, modeling and acquiring such knowledge are the centraltopics of this paper. Because of the tremendous complexity in knowledge of human beings, weintroduce a top-level ontology of human beings from the perspectives of psychology, sociology,physiology and pathology. This ontology is not only an explicit conceptualization of humanbeings, but also an efficient way of acquiring and organizing relevant knowledge.

关键词： knowledge of human beings ontology top-level ontology of human beings psychology sociology physiology pathology knowledge acquisition

Pre-trained models for natural language processing: A survey

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Science China(Technological Sciences) 2020年第10期63卷 1872-1897页

作者： QIU XiPeng SUN TianXiang XU YiGe SHAO YunFan DAI Ning HUANG XuanJing School of Computer Science Fudan UniversityShanghai 200433China Shanghai key laboratory of intelligent information processing Shanghai 200433China

Recently, the emergence of pre-trained models(PTMs) has brought natural language processing(NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language representation learning and its research progress. Then we systematically categorize existing PTMs based on a taxonomy from four different perspectives. Next,we describe how to adapt the knowledge of PTMs to downstream tasks. Finally, we outline some potential directions of PTMs for future research. This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.

关键词： deep learning neural network natural language processing pre-trained model distributed representation word embedding self-supervised learning language modelling

A ε-indicator-based shuffled frog leaping algorithm for many-objective optimization problems

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Journal of Systems Engineering and Electronics 2020年第1期31卷 142-155页

作者： WANG Na SU Yuchao CHEN Xiaohong LI Xia LIU Dui College of Electronics and information Engineering Shenzhen UniversityShenzhen 518060China Guangdong key laboratory of intelligent information processing Shenzhen UniversityShenzhen 518060China

Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto *** tackle this issue,a series of indicatorbased multi-objective evolutionary algorithms(MOEAs)have been proposed to guide the evolution progress and shown promising *** paper proposes an indicator-based manyobjective evolutionary algorithm calledε-indicator-based shuffled frog leaping algorithm(ε-MaOSFLA),which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effectiveε-indicator as a fitness assignment scheme to press the population towards the Pareto *** with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives,the experimental results show thatε-MaOSFLA outperforms the competitors.

关键词： evolutionary algorithm many-objective optimization shuffled frog leaping algorithm(SFLA) ε-indicator

Syntax-guided text generation via graph neural network

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Science China(information Sciences) 2021年第5期64卷 67-76页

作者： Qipeng GUO Xipeng QIU Xiangyang XUE Zheng ZHANG Shanghai key laboratory of intelligent information processing School of Computer ScienceFudan University NYU Shanghai and AWS Shanghai AI Lab

Text generation is a fundamental and important task in natural language processing. Most of the existing models generate text in a sequential manner and have difficulty modeling complex dependency structures. In this paper, we treat the text generation task as a graph generation problem exploiting both syntactic and word-ordering relationships. Leveraging the framework of the graph neural network, we propose the word graph model. During the process, the model builds a sentence incrementally and maintains syntactic integrity via a syntax-driven, top-down, breadth-first generation process. Experimental results on both synthetic and real text generation tasks show the efficacy of our approach.

关键词： text generation deep learning graph neural network dependency parsing

RGB-D Hand-Held Object Recognition Based on Heterogeneous Feature Fusion

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Journal of Computer Science & Technology 2015年第2期30卷 340-352页

作者：吕雄蒋树强 Luis Herranz 王双 key laboratory of intelligent information processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Object recognition has many applications in human-machine interaction and multimedia retrieval. However, due to large intra-class variability and inter-class similarity, accurate recognition relying only on RGB data is still a big challenge. Recently, with the emergence of inexpensive RGB-D devices, this challenge can be better addressed by leveraging additional depth information. A very special yet important case of object recognition is hand-held object recognition, as manipulating objects with hands is common and intuitive in human-human and human-machine interactions. In this paper, we study this problem and introduce an effective framework to address it. This framework first detects and segments the hand-held object by exploiting skeleton information combined with depth information. In the object recognition stage, this work exploits heterogeneous features extracted from different modalities and fuses them to improve the recognition accuracy. In particular, we incorporate handcrafted and deep learned features and study several multi-step fusion variants. Experimental evaluations validate the effectiveness of the proposed method.

关键词： RGB-D hand-held object recognition heterogeneous features fusion