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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Buchanan, Edgar Hickinbotham, Simon Dubey, Rahul Friel, Imelda Colligan, Andrew Price, Mark Tyrrell, Andy M. School of Physics Engineering and Technology University of York Intelligent Systems & Robotics Research Group United Kingdom Missouri State University Computer Science United States School of Mechanical & Aerospace Engineering Queen's University Belfast United Kingdom

ISBN: (纸本)9798350308365

For a long time engineering design has relied on human engineers manually crafting and refining designs using their expertise and experience. In Bio-inspired Evolutionary Development (EvoDevo), generative algorithms are employed to investigate a broader design space that may go beyond what human engineers have considered. Previous literature has demonstrated the use of quality and diversity (QD) algorithms in evolutionary approaches to drive the process to better quality solutions. This paper provides a study to understand the effects of using QD algorithms in EvoDevo processes for engineering design. This paper also analyses the impact of using different behavioural characterisations (BC) in the performance of the quality of the solutions found. The results demonstrate that quality and diversity algorithms can find better solutions than other EAs for engineering design problems. It was also found that the characterisation of the BC is important to get the best results. © 2024 IEEE.

关键词： evodevo generative design neural networks quality diversity structural engineering

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Graph Enhanced Transformer for Aspect Category Detection

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Journal of computer science & Technology 2023年第3期38卷 612-625页

作者：陈晨王厚峰朱晴晴柳军飞 Office of the Cyberspace Affairs Commission Peking UniversityBeijing 100871China School of Computer Science Peking UniversityBeijing 100871China School of Software and Microelectronics Peking UniversityBeijing 100871China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China

Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.

关键词： aspect based sentiment analysis aspect category detection hierarchical multi-label classification transformer network

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Digital Currency Abnormal Detection Based on Graph Neural Network

Digital Currency Abnormal Detection Based on Graph Neural Ne...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Que, Xingle Cheng, Yang Bai, Yunfei Lu, Dandan Southwest University of Science and Technology School of Computer Science and Technology Mianyang China Southwest University of Science and Technology Dept. Engineering Technology Center Mianyang China

ISBN: (纸本)9798331527624

In recent years, with the rapid development of the digital currency market, the security issues of digital currency transactions have become increasingly prominent, and abnormal transaction detection has become an important technical means to ensure the security of the financial system. Traditional detection methods face many challenges in dealing with complex blockchain data, as they cannot fully utilize the inherent structural information of transaction data, and are difficult to handle the problem of imbalanced categories. Accordingly, this study proposes a GNN (Graph Neural Network)-based abnormal transaction detection method, which models transaction data as a graph structure, extracts transaction features using Graph Convolutional Network (GCN) and uses small sample processing techniques to alleviate data imbalance. The proposed method showed that the experimental results could effectively improve the detection performance of abnormal transactions after multiple rounds of training, especially in terms of AUC evaluation indicators, which verifies its potential application in the detection of abnormal transactions in digital currencies. This study provides a novel and effective solution for anomaly detection in complex network data and points out the direction for future research. © 2024 IEEE.

关键词： Graph neural networks

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Energy Shaping Based Nonlinear Anti-Swing Controller for Double-Pendulum Rotary Crane with Distributed-Mass Beams

Energy Shaping Based Nonlinear Anti-Swing Controller for Dou...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： Li, Gang Ma, Xin Li, Zhi Li, Yibin Shandong University Center of Robotics School of Control Science and Engineering Jinan250061 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Rotary cranes usually transfer the heavy mass and large payloads with distributed mass beam in practice. As a typical underactuated system, the dynamic model is very complicated and challenging due to the actuated boom luffing motion and underactuated distributed mass beam payload swing vibration. To simplify the dynamic model, the existing control method regards the payload as the mass point, which ignores the geometry of the payload. This paper proposes an energy shaping based nonlinear anti-swing controller for double-pendulum rotary crane with distributed-mass beams. The rotary crane dynamic model is established based on the Lagrange's method. After analysis the energy function, a nonlinear anti-swing controller is designed for the rotary crane. It successfully solves the boom positioning and payload anti-swing problems with a distributed mass beam payload. LaSalle's invariance theorem and Lyapunov technology are helped to analysis the stability of the rotary crane control system. Finally, the proposed controller shows effective and robust control performance in the simulation verification. © 2022 IEEE.

关键词： Controllers

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A Corpus and Method for Chinese Named Entity Recognition in Manufacturing 30

A Corpus and Method for Chinese Named Entity Recognition in ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Li, Ruiting Wang, Peiyan Wang, Libang Yang, Danqingxin Cai, Dongfeng School of Computer Science Shenyang Aerospace University Shenyang China Liaoning Professional Technology Innovation Center on Knowledge Engineering and Human-Computer Interaction Shenyang China

ISBN: (纸本)9782493814104

Manufacturing specifications are documents entailing different techniques, processes, and components involved in manufacturing. There is a growing demand for named entity recognition (NER) resources and techniques for manufacturing-specific named entities, with the development of smart manufacturing. In this paper, we introduce a corpus of Chinese manufacturing specifications, named MS-NERC, including 4,424 sentences and 16,383 entities. We also propose an entity recognizer named Trainable State Transducer (TST), which is initialized with a finite state transducer describing the morphological patterns of entities. It can directly recognize entities based on prior morphological knowledge without training. Experimental results show that TST achieves an overall 82.05% F1 score for morphological-specific entities in zero-shot. TST can be improved through training, the result of which outperforms neural methods in few-shot and rich-resource. We believe that our corpus and model will be valuable resources for NER research not only in manufacturing but also in other low-resource domains. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Transducers

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Trajectory Design and Resource Allocation for UAV-Assisted Computation Offloading 7

Trajectory Design and Resource Allocation for UAV-Assisted C...

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7th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2024

作者： Xu, Yunxin Wang, Jiale Wang, Junhao Que, Xingle Lu, Dandan School of Computer Science and Technology Southwest University of Science and Technology Mianyang China Dept. Engineering Technology Center Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350350760

This paper studies air-ground cooperative communication based on unmanned aerial vehicles (UAVs) and device-to-device (D2D) technology. This field has become a research hotspot due to its susceptibility to passive eavesdropping and privacy leakage. To enhance data transmission security, this paper proposes a full-duplex UAV-assisted scheme to address potential ground eavesdropping risks in UAV-assisted computation offloading tasks. The scheme transmits interference signals while receiving computation tasks to counteract eavesdroppers. Given the limited energy of UAVs and the inadequacies of existing energy optimization research, this paper establishes a UAV-assisted mobile edge computing (MEC) communication system model. To address the non-convex optimization problem within the model, a successive convex approximation algorithm is employed for simplification and decomposition. User scheduling, power allocation, and UAV trajectory design subproblems are solved through an alternating iteration method. Simulation results validate that the proposed method can effectively improve the system's secure communication performance even in the presence of uncertain eavesdropper locations. ©2024 IEEE.

关键词： Convex optimization

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A Nonlinear Coupling-Based Motion Trajectory Planning Method for Double-Pendulum Rotary Crane Subject to State Constraints

A Nonlinear Coupling-Based Motion Trajectory Planning Method...

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International Conference on Applied Nonlinear Dynamics, Vibration and Control, ICANDVC 2021

作者： Li, Gang Ma, Xin Li, Zhi Li, Yibin Center for Robotics School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9789811659119

Fast and accurate positioning while suppressing swing is pivotal to efficient and safe operation for the rotary cranes. The problem becomes more complicated when a double-pendulum effect exists for some certain payloads. This paper proposes a nonlinear coupling-based motion trajectory planning method for double-pendulum rotary crane subject to state constraints. The proposed control method consists of two parts: a positioning reference component and a swing-eliminating component. The positioning reference angular acceleration trajectory is derived from a trapezoid-like function with suitable duration, which considers state constraints. The swing-eliminating angular acceleration trajectory is designed based on the nonlinear coupling relationship among the boom luffing motion, the hook swing and the payload swing. State constraints can be guaranteed by adjusting angular acceleration (deceleration) change duration, hold duration and constant velocity duration. The Lyapunov technique and LaSalle’s invariance theorem are used for stability analysis. Simulation results verify the control performance of the proposed nonlinear coupling-based motion trajectory planning method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trajectories

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Distributed Truss Computation in Dynamic Graphs

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Tsinghua science and Technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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An Analysis of the Elevator Failure Prediction System with BigDL Time Series Forecasting Models 15

An Analysis of the Elevator Failure Prediction System with B...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rajagopal, Madhumitha Jayaraman, Ashok Kumar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Center of Excellence HCLTech Artificial Intelligence Chennai India

ISBN: (纸本)9798350370249

Predictive maintenance represents a revolutionary approach in industrial operations, aiming to predict failures before they occur, thus ensuring continuity and efficiency. Elevator systems, crucial for daily operations in numerous buildings worldwide, are particularly suited for this technology. Our research introduces an innovative Elevator Failure Prediction System that leverages Chronos, a component of the BigDL library designed for large-scale time series forecasting. In this paper, we significantly contribute to the enhancement of predictive maintenance strategies, offering a robust framework for anticipating and mitigating potential elevator malfunctions. This study mainly focuses on three sophisticated deep learning models tailored for time series prediction: Sequence-to-Sequence (Seq2Seq) Forecaster, Long Short-Term Memory (LSTM) Forecaster, and Temporal Convolutional Network (TCN) Forecaster. Each model is quantitatively evaluated using Root Mean Square Error metrics across various training epochs, with a consistent lookback period of 60 and 120 time steps. This systematic assessment not only delineates the strengths and limitations of each model in predicting elevator failures but also lays the groundwork for future technological advancements in the elevator industry. Through this experimental analysis, we indicate that the Seq2Seq Forecaster performs well in terms of error rate for a look back period of 60 time steps whereas TCN Forecaster performs well in terms of error rate for a look back period of 120 time steps. However, in both the cases, the LSTM Forecaster performs well in terms of time. © 2024 IEEE.

关键词： bigdl chronos long short-term memory forecaster and temporal convolutional network forecaster Predictive maintenance sequence-to-sequence forecaster time series forecasting

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Self-adaptive label filtering learning for unsupervised domain adaptation

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Frontiers of computer science 2023年第1期17卷 225-227页

作者： Qing TIAN Heyang SUN Shun PENG Tinghuai MA School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China

1 Introduction As an emerging machine learning paradigm,unsupervised domain adaptation(UDA)aims to train an effective model for unlabeled target domain by leveraging knowledge from related but distribution-inconsistent source *** of the existing UDA methods[2]align class-wise distributions resorting to target domain pseudo-labels,for which hard labels may be misguided by misclassifications while soft labels are confusing with trivial noises so that both of them tend to cause frustrating *** overcome such drawbacks,as shown in Fig.1,we propose to achieve UDA by performing self-adaptive label filtering learning(SALFL)from both the statistical and the geometrical perspectives,which filters out the misclassified pseudo-labels to reduce negative ***,the proposed SALFL firstly predicts labels for the target domain instances by graph-based random walking and then filters out those noise labels by self-adaptive learning strategy.

关键词： filtering resort overcome

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