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Automation, Robotics and Computer engineering (ICARCE), International Conference on

作者： Daxiong Ji Xinwei Wang Minghui Xu Qiang Zhang Sanjay Sharma Robert Sutton Institute of Marine Electronics & Intelligent Systems Ocean Collage Zhejiang University Donghai Lab Zhoushan China Intelligent Autonomous Control Systems School of Engineering Computing and Mathematics University of Plymouth Plymouth UK

In consideration of the trajectory tracking problem of autonomous underwater vehicle (AUV) under external current disturbance, an adaptive linear active disturbance rejection control (LADRC) method is proposed. Unlike the conditional LADRC using fixed parameters for extended state observer, the proposed controller makes use of the error of estimation and automatically adjusts the parameter online. As shown in the simulation, the adaptive LADRC estimates the external current disturbance effectively, and tracks the 3D trajectory better compared with traditional LADRC.

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

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Computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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A Loop Optimization Method for Dataflow Architecture

A Loop Optimization Method for Dataflow Architecture

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Zhihua Fan Wenming Li Tianyu Liu Shengzhong Tang Zhen Wang Xuejun An Xiaochun Ye Dongrui Fan State Key Lab of Processors Institute of Computing Technology CAS Beijing China School of Computer and Control Engineering UCAS Beijing Beijing China

Dataflow architecture is a promising parallel computing platform that provides high performance, high efficiency and flexibility. Since the execution of a loop is usually the most time-consuming in a program, extracting sufficient loop-level parallelism is an essential task of dataflow architecture. However, existing methods often result in low execution performance and poor hardware utilization due to the imbalance among dataflow graph nodes and the non-preemptive mechanism. To tackle these problems, this paper makes three contributions: 1) A highly effective dataflow graph balancing method is developed to improve the utilization. 2) An enhanced dataflow execution model is proposed, which adapts a novel preemptive mechanism to further improve the hardware utilization and performance. 3) A decoupled dataflow architecture is designed to efficiently support our proposed dataflow execution model. Experiment shows that our methods can achieve an average of 2.61 ×performance improvement and 2.5 ×utilization improvement compared to the state-of-the-art methods with acceptable overhead.

关键词： Adaptation models Costs Optimization methods Computer architecture Hazards Hardware Task analysis

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OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation

arXiv

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arXiv 2023年

作者： Teng, Fei Zhang, Jiaming Peng, Kunyu Wang, Yaonan Stiefelhagen, Rainer Yang, Kailun The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The Institute for Visual Computing ETH Zurich Zurich8092 Switzerland

Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation. However, two significant issues arise: (1) The extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent agents. (2) A relative displacement difference exists in the data collected by different micro-lenses. To address these issues, we propose an Omni-Aperture Fusion model (OAFuser) that leverages dense context from the central view and extracts the angular information from sub-aperture images to generate semantically consistent results. To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective Sub-Aperture Fusion Module (SAFM). This module efficiently embeds sub-aperture images in angular features, allowing the network to process each sub-aperture image with a minimal computational demand of only (∼1GFlops). Furthermore, to address the mismatched spatial information across viewpoints, we present a Center Angular Rectification Module (CARM) to realize feature resorting and prevent feature occlusion caused by misalignment. The proposed OAFuser achieves state-of-the-art performance on four UrbanLF datasets in terms of all evaluation metrics and sets a new record of 84.93% in mIoU on the UrbanLF-Real Extended dataset, with a gain of +3.69%. The source code for OAFuser is available at https://***/FeiBryantkit/OAFuser. Impact Statement-To solve the data abundance problem, we have reduced the significant computational consumption of light field cameras while not introducing any additional parameters. The proposed method has practical value for the deployment and application of light field cameras. The proposed

关键词： Semantic Segmentation

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INTELLIGENT TRACKING AND NAVIGATING MOVING OBJECTS IN A SMART ENVIRONMENT USING IOT NETWORK 1

INTELLIGENT TRACKING AND NAVIGATING MOVING OBJECTS IN A SMAR...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Parvathi, R. Kumar, T.Ch. Anil Nandhakumar, A. Arshad, Haqqani Singh, Ajith B. Department of Information Technology School of Computing Science Hindustan Institute of Technology and Science Padur Chennai India Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Vadlamudi Dt. Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Dhaanish Ahmed Institute of Technology Chennai India Department of Mechanical Engineering Yanbu Industrial Collge Yanbu Western Region Saudi Arabia Department- Instrumentation and Control Engineering Sri Krishna College of Technology Coimbatore India

ISBN: (纸本)9781607685395

Internet of things (IoT) constitutes the combinations of sensors, controllers, actuators, connectivity. Tracking and navigating moving objects are crucial tasks for security as well as a secure and smart environment. By using the sensory data, location of objects can be identified then that sensory data will be transferred to controllers for the further processes. One pivot role of the system is to identify the trajectory and location of the object using the speed, velocity, acceleration, maps etc and this can be done by machine learning programs. Analyzing service required, prediction of the trajectory and real-world knowledge of maps or locations, To avoid the loop-carry dependency, service request parameters are dynamically changed to locate relevant virtual objects. In order to track moving items, this study provides an architecture based on IoT self-aware environment technology that facilitates the tracking and allocation of relevant virtual objects. © The Electrochemical Society

关键词： IoT loop-carry dependency machine learning object identification self-aware environment sensory data

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Improved Action Recognition Algorithm for Video Surveillance Based on PoseC3D

Improved Action Recognition Algorithm for Video Surveillance...

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International Conference on Computer and Communications (ICCC)

作者： Ruifeng Guo Chao Yang Changyi Deng Ying Bai Xiaoxing Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

The precise recognition of human action is of great significance for intelligent monitoring and video analysis. Previous researchers have utilized many methods to detect human action, but the accuracy and robustness of these methods remain to be improved. Skeleton-based action recognition methods have reported high accuracy and robustness, but these methods have not fully utilized multiple modalities for feature fusion. In order to solve the above problems, a multi-modality feature fusion based video surveillance personnel action recognition method is proposed by combining the two methods. The SlowOnly network is used to extract RGB modality features. The Faster R-CNN and Lite-HRNet networks are used to obtain skeleton modality data. The PoseC3D network is used to extract skeleton modality features. The early and late fusion of RGB modality features and skeleton modality features are performed. Then the action recognition results of video surveillance personnel are obtained. The experimental results on the NTU RGB+D public dataset under the X-Sub standard show the following points. In the action recognition model based on a single skeleton modality, PoseC3D has a higher recognition accuracy than GCN methods, reaching 92.8%. In the action recognition model based on RGB modality, recognition accuracy has reached 95.4%. The action recognition model based on multi-modality feature fusion has a higher recognition accuracy than the recognition model based on a single modality, reaching 96.1%.

关键词： Accuracy Computational modeling Feature extraction Video surveillance Skeleton Robustness Personnel Standards Monitoring

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Research on Power Load Forecasting Based on Deep Learning

Research on Power Load Forecasting Based on Deep Learning

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Lanxin Lin Jingxin Yao Kun Wang Zhejiang Gongshang University Sussex Artificial Intelligence Institute Hangzhou China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao Hebei Chinese academy of sciences University of Chinese Academy of Sciences Shenyang institute of computing technology Shenyang China

In order to fully explore the time-series correlation of power load data and improve the prediction accuracy of power load, this paper proposes a neural network-based deep learning approach for power load prediction. Firstly, the relevant electric power data are obtained and divided into appropriate sample sizes, and the samples are normalized; then, a prediction model based on LSTM is built to explore the correlation between different features, and the corresponding model of this neural network is further trained and validated on the data test set; finally, a comparison between LSTM and other algorithms such as SVM, ANN, GAOS and GM are performed. The results show that the LSTM prediction algorithm can better track the trend of power load change, with higher prediction accuracy and efficiency.

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Real-time In-situ Defect Detection in 3D Printing of Continuous Fiber-Reinforced Composites 4

Real-time In-situ Defect Detection in 3D Printing of Continu...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yue, Zhijian Shen, Zhen Dong, Xiangyang Fang, Qihang Wang, Weixing Dong, Xisong Xiong, Gang Wang, Fei-Yue University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing100190 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266109 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Chinese Academy of Sciences State Key Laboratory for Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

With the development of continuous fiber-reinforced composites (CFRCs) 3D printing technology, timely, efficient and accurate detection of fiber path defects is essential for ensuring product quality and performance. However, challenges still need to be addressed in this field, such as inadequate training data coverage and the complexity of fiber path structures. These factors hinder the capability of traditional methods to accurately assess the status of printed products. Therefore, developing accurate and robust fiber path defect detection techniques is crucial for CFRCs 3D printing technology. In this paper, we propose a precise detection method for fiber path defects in CFRCs 3D printing using the YOLOv7 object detection model combined with a Squeeze-and-Excitation (SE) attention mechanism. By introducing the SE attention mechanism, we enhance the perception capability of the YOLOv7 model for crucial features, thereby improving the accuracy of fiber path defect detection. The experimental results show that the YOLOv7 model, when augmented with an attention mechanism, achieves an average accuracy of 93.9% in identifying fiber path defects during the printing process. Compared to models without this attention mechanism, the results show that there is 10.7% increase, with a notably enhanced learning capacity during the training phase, which substantiates the efficacy and practicality of the proposed approach. © 2024 IEEE.

关键词： Fiber reinforced plastics

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A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Lu, Yao Zhu, Yutao Li, Yuqi Xu, Dongwei Lin, Yun Xuan, Qi Yang, Xiaoniu Zhejiang University of Technology Binjiang Institute of Artificial Intelligence Hangzhou310056 China Chinese Academy of Sciences Institute of Computing Technology Beijing China Harbin Engineering University College of Information and Communication Engineering Harbin China Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou China Peng Cheng Laboratory PCL Research Center of Networks and Communications Shenzhen518000 China Hangzhou310056 China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

With the successful application of deep learning in communications systems, deep neural networks are becoming the preferred method for Automatic Modulation Recognition (AMR). Although these AMR models yield impressive results, they often come with high computational complexity and large model sizes, which hinders their practical deployment in communication systems. To address this challenge, we propose a novel layer pruning method, PSR. Specifically, we decompose the AMR model into several consecutive blocks, each containing consecutive layers with similar semantics. Then, we identify layers that need to be preserved within each block based on their contribution. Finally, we reassemble the pruned blocks and fine-tune the compact model. Extensive experiments on five datasets demonstrate the efficiency and effectiveness of PSR over a variety of state-of-the-art baselines, including layer pruning and channel pruning methods. © 2015 IEEE.

关键词： Signal modulation

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Reliability modeling and verification of communication algorithm flow for intelligent control system 4

Reliability modeling and verification of communication algor...

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4th International Conference on Advanced Electronic Materials, Computers and Software engineering, AEMCSE 2021

作者： Ran, Wu Jiajia, Wang Beijing Air Traffic Control Engineering Technology Co. Ltd Beijing China Applied Mathematics Research Center of CASIC Beijing Jinghang Research Institute of Computing and Communication Beijing China

ISBN: (纸本)9781665415965

The intelligent system controls the subsystems of each aircraft in real time to ensure the normal development of the tasks of the aircraft system. In the process of system communication, the control system is required to strictly control the correctness of the control algorithm flow and the accuracy of the control sequence in order to ensure the safety of the aircraft system. This paper uses UPPAAL, a formal model detection tool based on time automata theory, to formally model and validate the algorithm flow of an intelligent system and a subsystem. First, the algorithm flow of the intelligent system is analyzed, and then it is formally modeled using the time automaton method. Second, the properties that need to be verified are extracted from the algorithm flow and described with the formal language BNF. Finally, the function and performance correctness are automatically verified using the UPPAAL model detection tool. The experimental results verify that the intelligent system meets the security, accessibility and activity requirements. © 2021 IEEE.

关键词： Intelligent systems

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