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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

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Time Series Classification and Forecasting Method Selection in the Task of Intellectual Systems Development

Time Series Classification and Forecasting Method Selection ...

引用

international Russian automation conference (RusAutoCon)

作者： Pavel S. Sergeev Nadezhda G. Yarushkina Information Systems department Ulyanovsk State Technical University Ulyanovsk Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

Nowadays, it is important to implement intellectual analysis methods in information systems of different areas. Processes in such systems are described by temporal data and can be presented in the form of time series. While analyzing those time series, one of the main tasks is forecasting. There are many forecasting methods and models known. This variety poses for a researcher a problem of choice of an efficient one. This paper proposes the model of the forecasting method selection based on valuable time series characteristics. The proposed model can be implemented as a multiclass classifier via neural networks such as a multilayer perceptron. Different multilayer perceptron architectures are presented in this paper. A comparison of their performances using the KFold method is also provided.

关键词： Training automation Accuracy Time series analysis Neural networks Multilayer perceptrons Predictive models Classification algorithms Forecasting Information systems

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Development of an IoT-Integrated Automated Air Exchanger for Indoor Air Management in Computer Laboratories

Development of an IoT-Integrated Automated Air Exchanger for...

引用

robotics and Technologies for Industrial automation (ROBOTHIA), IEEE international conference on

作者： Leonard Nathaniel S. Majaducon Jose Ryandale D. Gonzaga Joost C. Besonia Glenda S. Guanzon Adha Theza Naputol Jopearlson Icy Ituriaga Department of Computer Engineering University of San Agustin Iloilo City Iloilo Philippines

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

The study develops an IoT-integrated automated air exchange system, addressing the issue on poor IAQ within computer laboratories. The developed system addresses gaps in previous solutions by introducing IoT integration and automated interventions to improve IAQ management within computer laboratories. This system is equipped with sensors capable of real-time monitoring of key indoor pollutants, such as carbon dioxide, particulate matter 2.5, and volatile organic compounds. Also, it comprises of an air exchange mechanism that automatically triggers when the pollutants' thresholds exceeded, ensuring timely and efficient way of reducing pollutants to maintain the optimal level of environment conditions. The air exchange mechanism comprises two fans, one for air intake and the other for air exhaustion to provide proper air circulation. Upon simulating real-world conditions in a prototype room model, results show that the system is effective in reducing pollutants' concentrations by measuring the levels of air parameters before and after operation of the system. With IoT-driven and automated intervention capabilities, the system's adaptable design and scalability provide a promising solution for improving the air quality of high-occupancy indoor spaces, allowing for a foundation to support broader real-world applications in industrial settings.

关键词： Volatile organic compounds Adaptation models Service robots Atmospheric modeling Scalability Laboratories Prototypes Real-time systems Energy efficiency Sensors

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Sound Source Localization in a 3D Multi-Surface Environment Using Learning-Based methods

Sound Source Localization in a 3D Multi-Surface Environment ...

引用

RSI/ISM international conference on robotics and Mechatronics (ICROM)

作者： Soroush Esfahanian Keyhan Rayati Mehdi Tale Masouleh Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

This study presents a three-dimensional localization of a ball drop in a multi-surface environment using cost-effective data acquisition devices, and proposes two learning-based methods to improve the baseline classical localization method based on GCC-PHAT-estimated TDOA values. The first proposed method employs a ball drop surface classification executed by Random Forest and XGBoost models to enhance localization performance, whereas the second method conducts region classification by using both basic and multi-branch neural network models, achieving improved and more efficient localization performance. A dataset of real-world sparse ball drop samples was collected in a controlled multi-surface environment for experimental execution, where results demonstrate that the two proposed methods improve the localization performance across various scenarios, with the region classification method significantly outperforming the baseline method, achieving a 17% reduction in the Mean Euclidean Distance error metric. Furthermore, the surface and region classification methods reduce the localization time compared to the baseline method by 50% and 97%, respectively. The obtained results reveal the potential of combining simple learning-based models with affordable microphone sensors for achieving an accurate localization of sparse audio samples in a multi-surface environment.

关键词： Location awareness Learning systems Visualization Artificial neural networks Data models Sensors Robots Random forests Microphones Context modeling

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Automatic Decision-Making for Unmanned Surface Vehicle Operation Based on CNN-RF Hybrid Model

Automatic Decision-Making for Unmanned Surface Vehicle Opera...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Yuting Bai Xinyi Xue Tingli Su Xuebo Jin College of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Automatic decision-making is crucial for the autonomous operation of unmanned surface vehicles, especially in effectively utilizing multi-source information for dynamic strategy selection. In this paper, we propose a CNN-RF hybrid model for unmanned surface vehicle operations. This model integrates CNN’s feature learning with RF’s nonlinear classification for comprehensive multi-source data modeling. Our approach includes meteorological data alongside state and obstacle information, enhancing decision-making reliability. Comparative simulations with traditional methods demonstrate that our model accurately predicts collision risks and performs effective avoidance, ensuring efficient navigation in complex environments.

关键词： Adaptation models Decision making Training data Predictive models Feature extraction Data models Vehicle dynamics Collision avoidance Autonomous vehicles Robots

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Network Anomaly Detection with Stacked Sparse Shrink Autoencoders and Improved XGBoost

Network Anomaly Detection with Stacked Sparse Shrink Autoenc...

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2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Guan, Ziyue Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Efficient and accurate identification of network anomalies is of great significance to the construction of network security systems in the information age. It is highly challenging to accurately detect abnormal behaviors in the increasing network data. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have poor detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. For network anomaly detection, this work proposes a new network anomaly detection method named SAXP, which integrates Stacked sparse shrink Autoencoders and a XGBoost model based on genetic simulated annealing Particle swarm optimization (GSPSO). Specifically, features extracted by stacked sparse shrink autoencoders are introduced into the XG Boost model for classification, and GSPSO is used to optimize the hyperparameters of XGBoost. Experimental results based on two real-life data sets demonstrate that the proposed SAXP achieves higher recognition accuracy than several state-of-the-art algorithms. © 2023 IEEE.

关键词： autoencoders feature extraction Network anomaly detection particle swarm optimization XGBoost

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Multi-Step Water Quality Prediction with Series Decomposition and Auto-Correlation

Multi-Step Water Quality Prediction with Series Decompositio...

引用

2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Yuan, Mingxing Yuan, Haitao Qiao, Junfei Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798350337020

Water quality prediction provides timely management to solve possible water environmental problems, which is of great importance. However, the following challenges exist: 1) The existence of noise in the water quality time series can lead to overfitting of nonlinear models;2) It is difficult to capture temporal dependencies in complex time series data;3) Long-term forecasting is difficult to achieve. To address the above difficulties, this work proposes a multi-step water quality prediction model, called SG-Autoformer, which combines the Savitzky-Golay filter, the inner series decomposition, and an auto-correlation mechanism. First, SG-Autoformer performs noise reduction on the water quality time series to suppress overfitting of nonlinear models. Second, it embeds series decomposition inside the encoder and decoder, which obtains more predictable components from complex time series for long-term prediction. Third, SG-Autoformer utilizes the auto-correlation mechanism to capture the time dependence and improve information utilization. Extensive experiments with real-world datasets show that SG-Autoformer outperforms other advanced prediction methods in terms of prediction accuracy. © 2023 IEEE.

关键词： auto-correlation multi-step prediction Savitzky-Golay filter series decomposition Water quality prediction

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Closed-Loop Subspace Identification for MIMO LPV System with Minimum Euclidean Distance of State Sequence

Closed-Loop Subspace Identification for MIMO LPV System with...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Liang Zhang Peng Deng Shuangjiang Du Tian Zhou Kaiming Yang Tao Huang State Key Laboratory of Mechanical Transmission & College of Mechanical and Vehicle Engineering Chongqing University Chongqing China Department of Mechanical Engineering Tsinghua University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

The rigid-flexible coupled variable mechanical system, as a typical multi-input multi-output (MIMO) system, poses significant challenges in system modeling, control design, theoretical analysis, and practical engineering implementation due to its complex dynamic characteristics and the interactions among multiple variables. To address these challenges, this paper proposes a subspace closed-loop identification method for MIMO linear parameter-varying (LPV) systems based on the minimum Euclidean distance of the state sequence. First, through the subspace closed-loop identification algorithm, a non-unique form of the linear time-invariant state-space (L TI-SS) model with the minimum Euclidean distance state sequence is obtained. Then, using the least squares method, a similarity transformation matrix is computed to convert the non-unique form of the LTI-SS model into the standard model. Finally, the global linear parameter-varying state-space (LPV -SS) model is obtained through cubic spline interpolation. The proposed method is experimentally validated on a single-degree-of-freedom ball screw test rig and simulated on a two-degree-of-freedom motion system. Through simulation and experimentation, the calibration determination coefficient of the LPV MIMO model is above 0.98, demonstrating the superior accuracy of the model. Compared with traditional modal standard models and controllable standard models, the proposed method requires fewer data to achieve the specified model accuracy, improves the computational efficiency of the identification process, and reduces experimental and data collection costs. This provides an effective solution for modeling rigid-flexible coupled variable mechanical systems.

关键词： Interpolation Accuracy Computational modeling Euclidean distance Transforms Systems modeling Data models Mechanical products Mechanical systems Standards

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IMU-Based Teleoperation for Supernumerary Robotic Limbs

IMU-Based Teleoperation for Supernumerary Robotic Limbs

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Electrical Engineering, Control and robotics (EECR), international conference on

作者： Keao Wang Shiyang Liu Chao Zeng Muye Pang Jing Luo School of Automation Wuhan University of Technology Wuhan China School of Information University of Hamburg Hamburg Germany

ISBN: (数字)9798350370003

ISBN: (纸本)9798350370010

This paper introduces a teleoperation framework for Supernumerary Robotic Limbs(SRLs) using an Inertial Measurement Unit(IMU) as the master device. The framework allows users to control the SRL by adjusting Euler angles, so as to provide an assistance in performing tasks. The research outlines the process of utilizing data from the IMU to obtain pose information, then transform this information into end position information for the SRL via the application of two distinct mapping relationships: the accumulation-based and the percentage-based methods. Joint information of the SRL is obtained by resolving the inverse kinematics and effectively realizing an IMU-based SRL teleoperation system. The efficacy of the IMU as a controller for teleoperation is verified using experimental trials which demonstrates positive outcomes. Though the errors of the percentage-based method are found to be slightly larger than that of the accumulation-based method, both mapping methods are effective in providing accurate tracking performance.

关键词： Performance evaluation Industries Accuracy Measurement units Service robots Refining Transforms

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Tunes by Technology: A Comprehensive Survey of Music Generation models

Tunes by Technology: A Comprehensive Survey of Music Generat...

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Cognitive robotics and Intelligent Systems (ICC - ROBINS), international conference on

作者： Mohammed Johar Pathariya Pratyush Basavraj Jalkote Aniket Maharudra Patil Abhishek Ashok Sutar Rajashree L. Ghule AI & DS Department VPKBIET Baramati Pune India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Notable developments in Artificial Intelligence-based music production have brought about a transformation in the field of musical composition. The aim of this article is to give a summary of the different models and techniques used in AI-driven music production. It explores the need for these developments, highlighting the shortcomings of traditional music production methods, which often call for a significant investment of time and expertise and presenting Artificial Intelligence (AI) as a revolutionary solution to these challenges. Several models are examined in the review, including Transformer Architecture, Long Short-Term Memory (LSTM), Generative Adversarial Network (GAN) and many more. This study aims to give a comprehensive overview of Music generating techniques and their potential to improve the efficiency, accessibility, and collaborative nature of music creation within the musical environment.

关键词： Surveys Visualization Reviews Music Production Transformers Generative adversarial networks

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XSS Attack Detection methods Based on XLNet and GRU 4

XSS Attack Detection Methods Based on XLNet and GRU

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4th international conference on robotics, Control and automation Engineering (RCAE) / 4th international conference on Advanced Mechanical and Electrical Engineering (AMEE)

作者： Luo, Jing Xu, Guoqing Wuhan Inst Technol Sch Comp Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9781665427302

With the progress of science and technology and the development of Internet technology, Internet technology has penetrated into various industries in today's society. But this explosive growth is also troubling information security. Among them, XSS (cross-site scripting vulnerability) is one of the most influential vulnerabilities in Internet applications in recent years. Traditional network security detection technology is becoming more and more weak in the new network environment, and deep learning methods such as CNN and RNN can only learn the spatial or timing characteristics of data samples in a single way. In this paper, a generalized self-regression pretraining model XLNet and GRU XSS attack detection method is proposed, the self-regression pretrained model XLNet is introduced and combined with GRU to learn the time series and spatial characteristics of the data, and the generalization capability of the model is improved by using dropout. Faced with the increasingly complex and ever-changing XSS payload, this paper refers to the character-level convolution to establish a dictionary to encode the data samples, thus preserving the characteristics of the original data and improving the overall efficiency, and then transforming it into a twodimensional spatial matrix to meet XLNet's input requirements. The experimental results on the Github data set show that the accuracy of this method is 99.92 percent, the false positive rate is 0.02 percent, the accuracy rate is 11.09 percent higher than that of the DNN method, the false positive rate is 3.95 percent lower, and other evaluation indicators are better than GRU, CNN and other comparative methods, which can improve the detection accuracy and system stability of the whole detection system. This multi-model fusion method can make full use of the advantages of each model to improve the accuracy of system detection, on the other hand, it can also enhance the stability of the system.

关键词： Cross-site scripting network security deep learning text classification XLNet XSS attacks

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