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Harnessing Large Language models for Temporal Knowledge Graph Forecasting with Multi-view Prompting

Harnessing Large Language Models for Temporal Knowledge Grap...

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Artificial Intelligence, robotics, and Communication (ICAIRC), IEEE international conference on

作者： Da Zhang Tong Xu Fake Lin Jiayue Chen University of Science and Technology of China Hefei China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

In recent years, Temporal Knowledge Graphs (TKGs) have attracted considerable attention from researchers. An important research direction is forecasting future events in TKGs based on historical information, i.e. TKG forecasting. Existing methods primarily rely on modeling temporal relationships between entity IDs to make predictions, which brings limitations in understanding the entity content and the relationships between entities. Fortunately, the emergence of Large Language models (LLMs) has brought new insights into solving this problem. In this paper, we propose a novel LLM-enhAnced TKG forEcasting (LATE) model. Specifically, we first design multi-view prompts that enable LLMs to generate comprehensive descriptions for entities as external knowledge. Then, we get the enhanced representations of entities based on the descriptions and we further design a contrastive learning strategy to reinforce this process. Finally, we construct a TKG forecasting model based on self-attention mechanisms and dual-pattern LSTM-based temporal modeling to accomplish the forecasting task. Experiments on three datasets demonstrate the effectiveness and superiority of our model compared with several state-of-the-art baseline methods.

关键词： Large language models Noise Knowledge graphs Learning (artificial intelligence) Contrastive learning Bidirectional control Predictive models Encoding Forecasting Robots

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Demand Response based Zonal Load Forecasting for Electric Vehicle Charging Stations 2

Demand Response based Zonal Load Forecasting for Electric Ve...

引用

2nd international conference on Mechanical, Electronics, and Electrical and automation Control, METMS 2022

作者： Wang, Huimin College Of Automation College Of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781510654785

ISBN: (纸本)9781510654778

With the access to broad-based demand response (DR) resources such as large-scale electric vehicles (EVs), customers are responding to demand under the influence of various incentives in the electricity market, which will change load characteristics and affect load forecasting. This paper proposes a zonal load forecasting for EVs based on DR, using wavelet decomposition(WD) to form seasonal base load and DR dominant part load for similar days, dividing the area where EV charging stations are located into residential, work and public zones, using support vector machine regression methods to forecast for residential and work zones, and using time series models to forecast for seasonal For the public area, a time series model is used to forecast the seasonal base load, and a grey forecasting model is used to forecast the dominant part of the DR, and Markov correction is applied to the forecast results. By comparing with the traditional method, the proposed method proves its effectiveness and accuracy in forecasting the load of EV charging stations with demand response. © 2022 SPIE

关键词： Electric vehicles

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Towards Learning-Based Distributed Task Allocation Approach for Multi-Robot System

Towards Learning-Based Distributed Task Allocation Approach ...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Zakaria Chekakta Nabil Aouf Shashank Govindaraj Fabio Polisano Geert De Cubber City University of London United Kingdom Space Applications Services NV/SA Brussels Belgium Royal Military Academy Brussels Belgium

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

This paper introduces a novel application of Graph Convolutional Networks (GCNs) for enhancing the efficiency of the Consensus-Based Bundle Algorithm (CBBA) in multi-robot task allocation scenarios. The proposed approach in this research lies in the integration of a learning-based strategy to approximate the heuristic methods traditionally used for scoring in the CBBA framework. By employing GCNs, the proposed methodology aims to learn and predict the score function, which is crucial for task allocation decisions in multi-robot systems. This approach not only streamlines the allocation process but also potentially improves the accuracy and efficiency of task distribution among robots. The paper presents a detailed exploration of how GCNs can be effectively tailored for this specific application, along with results demonstrating the advantages of this learning-based approach over conventional heuristic methods in various simulated multi-robot task allocation scenarios.

关键词： Sequential analysis automation Accuracy Robot kinematics Prediction algorithms Approximation algorithms Resource management

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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Networks 24th

Cost-Efficient Multi-Objective Design of Miniaturized Microw...

引用

24th international conference on Computational Science (ICCS)

作者： Koziel, Slawomir Pietrenko-Dabrowska, Anna Leifsson, Leifur Reykjavik Univ Engn Optimizat & Modeling Ctr Dept Engn Menntavegur 1 IS-102 Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat Narutowicza 11-12 PL-80233 Gdansk Poland Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA

ISBN: (纸本)9783031637742;9783031637759

Designing microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when executed with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective MO technique for microwave passive components utilizing a machine learning (ML) framework with artificial neural network (ANN) surrogates as the primary prediction tool. In this approach, multiple candidate solutions are extracted from the Pareto set via optimization using a multi-objective evolutionary algorithm (MOEA) applied to the current ANN model. These solutions expand the dataset of available (EM-simulated) parameter vectors and refine the surrogate model iteratively. To enhance computational efficiency, we employ variable-resolution EM models. Tested on two microstrip circuits, our methodology competes effectively against several surrogate-based approaches. The average computational cost of the algorithm is below three hundred EM analyses of the circuit, with the quality of generated Pareto sets surpassing those produced by the benchmark methods.

关键词： Microwave design multi-objective optimization design automation machine learning neural networks surrogate modeling Pareto optimality

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Determination of Basic Probability Assignment Based on Membership Function and PCA

Determination of Basic Probability Assignment Based on Membe...

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international conference on robotics and automation Engineering (ICRAE)

作者： Aoxiang Zhao Junwei Li Huanyu Liu School of Artificial Intelligence Henan University Zhengzhou China

When using evidence theory to identify targets, how to generate basic probability assignment (BPA) based on the collected data is still an open issue. Based on this problem, a novel method to generate BPA based on membership function and principal component analysis (PCA) is proposed. First, this paper proposed a novel membership function and divide the data set into the training set and testing set, the membership model of each attribute is constructed based on the training set data. Secondly, the testing set sample is input to obtain the initial BPA of each attribute. Thirdly, the contribution rate of each attribute is used by PCA. Finally, the final BPA is obtained by discounting the initial BPA according to the contribution rate. The results of the experiment have demonstrated the classification accuracy under the Iris dataset is higher than other methods, and the average recognition rate of the Iris dataset is 97.3%.

关键词： Training automation Evidence theory Data models Principal component analysis Iris recognition Testing

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YOLO-ELD: Efficient and Lightweight Detection for UAV Aerial Imagery

YOLO-ELD: Efficient and Lightweight Detection for UAV Aerial...

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2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yan, Haimin Wang, Juncheng Kong, Xiangbo Tomiyama, Hiroyuki Graduate School of Science and Engineering Ritsumeikan University 1-1-1 Noji-Higashi Shiga Kusatsu Japan Chongqing Branch China United Network Communications Corporation Chongqing China Toyama Prefectural University Faculty of Information Engineering Department of Intelligent Robotics Kurokawa Imizu Toyama5180 Japan

ISBN: (纸本)9781665410205

Object detection in UAV imagery has become a hot topic in recent years. However, deploying real-time detection models on UAV platforms is highly challenging due to limited computational power and memory. Moreover, the large size of UAV-captured images, the small size of objects, and their dense distribution all impact detection efficiency. Many researchers have made a series of improvements to address these issues, but they have not maintained a good balance among model size, inference speed, and accuracy. To address above difficulties, this paper proposes an efficient and lightweight model that maintains moderate detection accuracy while achieving model lightweighting and reduced inference time. Concretely, considering the higher resolution of drone-captured images, we have designed a backbone with more lightweight downsampling modules, enhancing deployment efficiency on devices with limited resources. Additionally, this work incorporates a self-attention mechanism in the feature extraction component of the model, which significantly improves the ability to process critical areas in the image, crucial for detecting small-scale and densely distributed targets. Moreover, this work designs an IoU tailored for drone aerial images, which calculates losses by focusing on the shape and scale of bounding boxes, thereby enhancing the accuracy of bounding box regression. Additionally, it uses a ratio of scale factors to control the generation of auxiliary bounding boxes, which aids in loss calculation and accelerates convergence. Experimental results on the VisDrone 2019 dataset show that compared to existing detection methods used for drones, our model is more lightweight and efficient, while also achieving medium accuracy. Also, compared to our baseline method, YOLO-ELD reduces the number of model parameters by about 40%, increases the inference speed of the model by 10%, and also improves the precision of model by 3%. © 2024 IEEE.

关键词： Drones

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A fault detection method for AUV based on multi-scale spatiotemporal feature fusion 5

A fault detection method for AUV based on multi-scale spatio...

引用

5th international conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Xia, Shaoxuan Zhou, Xiaofeng Shi, Haibo Li, Shuai Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510662520

Autonomous underwater vehicles (AUVs) are important equipment for ocean development and exploration. To ensure the task implementation of AUV, faults shall be detected in time. We propose a fault detection method based on multi-scale spatiotemporal feature fusion (MSF) for the time-varying characteristics and multiple correlation characteristics of AUV monitoring data. First, we apply a variety of sampling and data processing methods to generate monitoring windows with different scales along the time axis. Then, a composite feature extraction method is proposed to obtain temporal and spatial features simultaneously, and a feature pyramid of temporal and spatial information is formed. We use bidirectional long short-term memory (BiLSTM) to obtain the time-series characteristics of a single monitoring variable, and convolutional neural networks (CNN) to obtain the implicit spatial relationship characteristics among multiple monitoring variables. Next, we use an adaptive feature fusion method to solve the inconsistency in different feature scales, which can adaptively suppress the possible conflict information of different scale features. Finally, we use a fully connected network to detect the fault of the fused features. The fault detection experiment of Haizhe AUV shows the effectiveness and superiority of the proposed method. © 2023 SPIE.

关键词： Autonomous underwater vehicles

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Hand-Eye Calibration of Surgical Robots Based on a BP Neural Network Optimized by Using an Improved Sparrow Search Algorithm

Hand-Eye Calibration of Surgical Robots Based on a BP Neural...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Jiaqi Zhu Weibo Ning Ye Yuan Hongjiang Chen Weijun Zhou Yecheng Tan Shuxing He Jun Hu Zhun Fan Shantou University Shantou China Hospital of Medical College of Shantou University Shantou China

ISBN: (纸本)9781665476881

Hand-eye calibration methods for surgical robots are employed to derive a transformation between the robot's base motor and visual coordinate systems. Accurately completing hand-eye calibration procedures provides an important guarantee that a surgical robot will exhibit positioning and execution accuracy sufficient for assisting surgeons in successfully completing surgical procedures. To improve the accuracy of robot hand-eye calibration methods based on backpropagation neural network (BPNN) models, we propose a modified BP neural network optimized using the sparrow search algorithm for hand-eye calibration model (TSSABPNN), which can enhance population initialization by applying tent mapping. Furthermore, we also design a new sliding 3D calibration tool. The sparrow search algorithm exhibits good local exploration ability, and we introduce a tent map with ergodic characteristics to initialize the sparrow population information, which further improves the network's global search ability and convergence rate. Finally, we experimentally analyze four calibration models: TSSABP NN model, a BP NN model optimized using a genetic algorithm of simulated annealing (GASABPNN), an unoptimized BP NN model, and the traditional singular value decomposition method. The results indicate that the proposed TSSABP NN model exhibits the maximum calibration precision and best robustness and iteratively converges faster.

关键词： Analytical models Medical robotics Three-dimensional displays Robot kinematics Sociology Artificial neural networks Robustness

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Digital control method for bucket truck based on PSO algorithm

Digital control method for bucket truck based on PSO algorit...

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IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Xu Jian Liang Zhang Shuangxue Sun Shengzu Li Gang Wang Jinbin Li Beinuo Huang Electric Power Research Institute State Grid Hubei Electric Power Co. Ltd Wuhan China Wuhan Power Supply Company State Grid Hubei Electric Power Co. Ltd Wuhan China School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

In the aspect of power maintenance, the control of bucket trucks has always been a difficult problem. It often requires operators to have enough experience. However, even so, due to the complex environment, it often takes a lot of time to control the end effector of the bucket truck to reach the appropriate position, which greatly reduces the efficiency of the operation and increases the accident rate during the entire process. To solve this problem, this article proposes a digital control method for bucket truck based on PSO algorithm. We first established a mathematical model of the bucket truck based on DH parameters and completed its forward kinematics derivation. On this basis, we introduced the PSO algorithm to complete the digital control of the bucket truck, solving the problem of high difficulty in obtaining its analytical solution. The experiment shows that the proposed method has high accuracy in both virtual and real environments, greatly improving the control efficiency of the bucket truck.

关键词： Digital control Mechatronics Trajectory planning Software algorithms Process control Kinematics Software Mathematical models Maintenance robotics and automation

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Design and Implementation of Multiple Mobile Robot Control System Based on MQTT 9

Design and Implementation of Multiple Mobile Robot Control S...

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9th international conference on Advances in Machinery, Materials Science and Engineering Applications, MMSE 2023

作者： Guo, Zheming Guan, Lirong Liu, Zhaoming Wang, Hongwei Cui, Long School of Mechanical Engineering Shenyang Ligong University Shenyang110159 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China Institutes of Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781643684383

Currently, the programming of multi robot control systems is difficult and requires high requirements for robot terminals. In response to the drawbacks of complex programming and easy interruption of message redundancy during communication in existing multi robot control systems, a multi robot control system based on MQTT is designed. MQTT is used as the communication protocol for multi robot communication, and a mobile robot experimental platform is built, this greatly reduces the redundancy of messages and the difficulty of manipulation in the control process of multiple robots. At the same time, the system has the function of action reproduction, which can record the actions of mobile robot terminals, reducing the difficulty of programming and debugging. Through experiments, the superiority of using MQTT protocol to control multiple robots has been verified. This system allows the robot to complete synchronous coordination work by only receiving commands from the upper computer and executing commands sent by the upper computer to the robot client, avoiding the need for the robot CPU to process a large amount of information and reducing the production cost of the robot. By controlling the robot's actions through a human-computer interaction platform, errors can be corrected in a timely manner, and control methods can be adjusted without worrying about the robot's actions making mistakes due to previous programs. © 2023 The Authors.

关键词： Mobile robots

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