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Research on Adaptive Evolution Architecture for Spacecraft control System 2

Research on Adaptive Evolution Architecture for Spacecraft C...

引用

2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Wang, Ruozhang Chen, Zhaohui Du, Janwei Beijing Institute of Control Engineering Software Center Beijing China Beijing Institute of Control Engineering Chief Engineer's Office Beijing China Beijing Institute of Control Engineering System Innovation Center Beijing China

ISBN: (纸本)9798350328363

With the continuous expansion of space missions and the growth on complexity of on-board software, improving the adaptive evolution ability of spacecraft control system becomes an important research issue. This paper focus on the adaptive evolution architecture of spacecraft software, which aims to solve the problems including software and hardware coupling, logic and information coupling, etc. And it introduces adaptive evolution layer to the architecture and enhances software flexibility through hierarchy and modularity. Besides, the dynamic parsing execution mechanism of evolution rule is proposed to provide the basic support for the dynamic adjustment through task scheduling, system parameters, and task logic. The architecture and mechanism have good application value for realizing adaptive evolution of spacecraft control system. © 2023 IEEE.

关键词： Memory architecture

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A Supervision Method for Online Violence Based on Blockchain and Deep Learning 2

A Supervision Method for Online Violence Based on Blockchain...

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2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Liu, Yu Wuhan Institute of City Department of Information Engineering Wuhan China

ISBN: (纸本)9798350328363

With the burgeoning development of the internet, online violence has metastasized into a formidable concern impeding cybersecurity and societal stability. This research proposes an innovative supervision methodology for online violence based on blockchain and deep learning technologies. The proposed technique harnesses the security, credibility, and traceability attributes of blockchain to ensure data integrity and authenticity alongside the efficiency, accuracy, and robustness of deep learning models for expedient text classification. Extensive experimentation on a 50,000-sample dataset validates that the introduced BCLD approach attains 0.95 accuracy and F1 score, outperforming baseline models by 4-5%. Additionally, balanced handling of positive and negative samples is demonstrated. The tamperproof nature of blockchain thwarts fraudulent data alteration. This pioneering methodology signifies a paradigm shift for constructing a wholesome online ecosystem. © 2023 IEEE.

关键词： Blockchain

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Proceedings - 2023 asia conference on advanced robotics, automation, and control engineering, arace 2023

Proceedings - 2023 Asia Conference on Advanced Robotics, Aut...

引用

2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

ISBN: (纸本)9798350328363

The proceedings contain 27 papers. The topics discussed include: a supervision method for online violence based on blockchain and deep learning;an iterative neighborhood blending method for multimodal product retrieval;artificial intelligence enterprise service quality evaluation system;Chinese continuous speech instruction recognition method based on improved convolutional neural network in noisy background;few-shot image semantic segmentation based on contextual information encoding strategy;indoor scene recognition based on YOLOv8 improved loss function;lightning short-impending prediction method based on SimVP spatio-temporal sequence prediction network;and MFEFNet: multi-scale feature extraction and fusion network for semantic segmentation of remote sensing images.

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Research and design of intelligent food delivery robot on campus under epidemic situation 2

Research and design of intelligent food delivery robot on ca...

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2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Xu, Yanhui Yang, Dunzhao Guangdong University of Science and Technology Dongguan China Guangdong University of Science and Technology Class 2 Electronic Information Engineering Grade 19 China

ISBN: (纸本)9798350328363

In order to improve the efficiency of campus food delivery under the epidemic situation, an intelligent food delivery robot with Raspberry PI (ROS system) and STM32F103VET6 as the control core be designed in this paper, which can achieve the goal of fully automatic food delivery in the dormitory. The Raspberry PI (ROS system) sends the food delivery command through WIFI, the system receives the command and responds, sends the speed command to the controller installed in the robot chassis and advances to the designated position according to the predetermined route, and sends the command to the system to prompt the students to take the food. The system mainly includes radar navigation, ultrasonic obstacle avoidance and voice prompt system and other modules to work together to complete automatic food delivery. © 2023 IEEE.

关键词： Motion planning

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MLEAT: An RL-based Approach for Vibration Damping System control 2

MLEAT: An RL-based Approach for Vibration Damping System Con...

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2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Ding, Ye Zhang, Xiaochuan Ren, Xiaoguang Wang, Xu Beijing China

ISBN: (纸本)9798350328363

Active vibration damping systems using PID controllers are widely applied in various industrial fields. The PID-controlled vibration damping systems can realize vibration damping depending on the appropriate PID parameters, which are not that easy to choose in practice. Therefore, adaptive tuning of PID controller parameters has been a focus of research in the industry. We propose a multi-level exploration adaptive tuning framework leveraging reinforcement learning algorithm, which enables adaptive tuning of PID parameters for vibration damping systems in response to vibration excitations, achieving the desired damping control effects. Without requiring prior knowledge, this algorithm ensures faster convergence and guarantees the stability of the damping system control. Experimental results in a simulated system demonstrate that our framework outperforms the baseline algorithm, achieving a performance improvement of 60% to 80% in the vibration damping system. © 2023 IEEE.

关键词： Three term control systems

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The Application of EMD-ARIMA-LSTM Neural Network in Stocks 2

The Application of EMD-ARIMA-LSTM Neural Network in Stocks

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2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Jianxun, Zhang Shaojie, Hu Lixu, Lu Tianjin University of Technology and Education Department of Information Technology Engineering Tianjin China

ISBN: (纸本)9798350328363

The Long short-term memory prediction model (LSTM) neural network model is widely used for time series prediction, with the advantage of being able to process some nonlinear long-term dependency data. However, there may be prediction lag and dependence on time series problems. However, traditional Autoregressive Integrated Moving Average model ( ARIMA ) have better processing performance for linear data and do not require a large amount of data for training. This article focuses on the issue of short-term stock prediction lag and excessive reliance on data. EMD-ARIMA-LSTM (Empirical Mode Decomposition Autoregressive Integrated Moving Average model Long Short Term Memory) based on attention mechanism is used to study and analyze the stock data of representative artificial intelligence enterprises in Tianjin. The research results indicate that the attention mechanism based EMD-ARIMA-LSTM improves the short-term prediction accuracy of stock prices by 1.7767% compared to traditional LSTM, which can provide reference and reference for relevant researchers. © 2023 IEEE.

关键词： Empirical mode decomposition

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An Iterative Neighborhood Blending Method For Multimodal Product Retrieval 2

An Iterative Neighborhood Blending Method For Multimodal Pro...

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2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Liu, Qixin He, Tingqin Zhou, Zisheng Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798350328363

Recently, the product matching and retrieval methods based on users' real needs are becoming a hot spot in academic research, especially in the era of big data, more efficient and accurate matching methods become more necessary. Therefore, this paper aims to design an iterative neighborhood blending method for multimodal product retrieval, this method fuses image and text features of products that can efficiently retrieve items of interest to users from massive product data. First, the feature extraction model is trained by the classification head in the training stage. secondly, post-processing methods such as pooling, batch normalization and feature normalization method are designed to obtain feature vectors of different dimensions. Thirdly, product matching and retrieval are realized by an improved iterative neighborhood mixing method. Finally, experiments on the Shopee-Price dataset demonstrate that the iterative neighborhood blending algorithm brings significant improvements to the multimodal product retrieval task compared to the state-of-art. © 2023 IEEE.

关键词： Iterative methods

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Human Activity Recognition Algorithm Based on ACNN-GRU 2

Human Activity Recognition Algorithm Based on ACNN-GRU

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2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Wang, Yueyan Tiangong University School of Artificial Intelligence Measurement and Control Laboratory Tianjin China

ISBN: (纸本)9798350328363

In the field of human activity recognition using wearable sensors, the accurate extraction of features from raw data and the development of an appropriate classification model are crucial for improving recognition accuracy. To respond to this and other challenges, we are put forward an ACNN-GRU deep learning model that amalgamate a convolutional neural network, a gated recursive unit, and an attention mechanism. The Convolutional Neural Network (CNN) virtually catch the spatial information in the sensor data, while the Gated Recursive Unit (GRU) models the temporal correspondence in the data sequence. By incorporating the attention mechanism, our model automatically learns and focuses on the salient features essential to activity recognition, resulting in more efficient extraction of fine-grained features from sensor data. Our method well identified six everyday activities: Walking, Standing, Sitting, Laying, Upstairs and Downstairs. Experimental results on the UCI open-source dataset demonstrate an impressive recognition rate of 97.65%, surpassing the performance of traditional classification models. © 2023 IEEE.

关键词： Extraction

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Artificial Intelligence Enterprise Service Quality Evaluation System 2

Artificial Intelligence Enterprise Service Quality Evaluatio...

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2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Yan, Tianan Zhang, Yifan Hua, Ruikai Institute of Scientific and Technical Information of China Engineering Center Beijing China Xi'an University of Finance and Economics Institute of Statistics Xi'an China Jiangnan Rural Commercial Bank Changzhou China

ISBN: (纸本)9798350328363

According to the characteristics of artificial intelligence industry and on the basis of understanding the meaning of service quality, this study constructs the service quality evaluation system of artificial intelligence enterprises through SERVQUAL and combined with the characteristics of artificial intelligence enterprises, so as to improve the service quality of artificial intelligence enterprises. 100 effective questionnaires containing the importance and satisfaction of 21 AI enterprise service quality influencing factors were designed and recovered, and the reliability, validity and IPA were tested. The analysis shows that through the above analysis, the secondary indicators located in the continuous maintenance area and key improvement area are divided into three aspects: hardware technology, user service and information security. The research results show that AI enterprises should set up products for different needs and formulate different marketing strategies according to the characteristics of users they serve. By formulating and implementing positive improvement strategies, artificial intelligence enterprises can achieve more effective and efficient performance. © 2023 IEEE.

关键词： Artificial intelligence

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RANet: Lightweight Crop Disease Identification Model based on Reparameterization and Attention Mechanism 2

RANet: Lightweight Crop Disease Identification Model based o...

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2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Li, Xiaopeng Li, Shuqin Northwest A&f University College of Information Engineering Xianyang China

ISBN: (纸本)9798350328363

A lightweight crop disease recognition model called RANet is proposed to address the issues of high parameter count, computational complexity, and poor real-time performance in using convolutional neural networks(CNNs) for crop leaf disease identification. Inspired by MobileNetV2, RANet introduces a reparameterizable basic module called RABlock. The structure of RABlock differs between training and inference, reducing model parameters and computational requirements while maintaining recognition accuracy and improving inference speed. Additionally, RANet incorporates the ECANet attention mechanism to enhance the model's focus on disease regions, further improving recognition performance. On a custom-built leaf disease dataset, RANet achieves an identification accuracy of 97.98% with only 1.8MB of parameters and a computational complexity of 0.179G. The inference time for a single-leaf disease image is 2.5ms. RANet outperforms lightweight models like MobileNetV3, striking a balance between recognition accuracy and speed. These results provide valuable insights for deploying lightweight deep learning models on edge devices. © 2023 IEEE.

关键词： Computational complexity

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