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Artificial Intelligence of Things and systems (AIoTSys), International Conference on

作者： Ziyi Zhou Ping Huang Song Zheng Zhezhuang Xu Zixiong Zhuang Education Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University and Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

Due to the similarity in appearance and dense deployment of devices in industrial environments, relying solely on machine vision makes it challenging for inspection robots to accurately identify similar devices. Although wireless signals from industrial internet of things (IoT) can serve as identification features, signal fluctuations impact the accuracy and efficiency of recognition. To address this issue, this paper proposes a proximity estimation algorithm with position adjustment for autonomous industrial inspection. The algorithm considers the spatial relationships between nodes and the topological relationship between the signal strength and variations among the nodes. By analyzing the topological relationship between the signal strength and variations among the nodes, the robot autonomously adjusts its position and selects the proximal node based on the spatial topology relationship between them. We have built an inspection platform using quadruped robots to evaluate the effectiveness of the experiments. The experimental results demonstrate that the algorithm further improves the efficiency of identifying proximal devices while ensuring the estimation accuracy of the algorithm.

关键词： Wireless communication Accuracy Fluctuations Service robots Estimation Inspection Topology Object recognition Quadrupedal robots Industrial Internet of Things

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Low-radioactivity ultrasonic hydrophone used in positioning system for Jiangmen Underground Neutrino Observatory

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Nuclear Science and Techniques 2022年第6期33卷 102-113页

作者： Duo Teng Jiang-Lai Liu Guo-Lei Zhu Yue Meng Yuan-yuan Zhang Tao Zhang Kai Luo Rui Li Jia-Qi Hui School of Marine Science and Technology Northwestern Polytechnical UniversityXi’an 710072China National Key Laboratory for Underwater Information Processing and Control Northwestern Polytechnical UniversityXi’an 710072China School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghai 200240China

To satisfy the accurate positioning requirement of the calibration source in the Jiangmen Underground Neutrino Observatory, a Tonpilz-type hydrophone with low radioactivity and high electroacoustics is *** radioactivity of the proposed hydrophone is strictly controlled by selecting pure raw materials, especially active piezoelectric ceramics. The electroacoustic performance of the hydrophone is improved by making structural optimization. High sensitivity and aimed directivity are achieved using 33-mode piezoelectric ceramic rings arranged in series and improvement in the radiating head of the Tonpilz hydrophone, respectively. All electroacoustic performances are studied through finite element *** simulations indicate that the electroacoustic performances of the hydrophones in linear alkylbenzene-based liquid scintillator can be approximately predicted according to the results in water because their differences caused by two types of acoustic media, water and liquid scintillator, are known. The tests show that the hydrophone prototype can achieve a maximum sensitivity of-209.3dB and a beamwidth of 132.2° at a frequency of 143 *** addition, eight hydrophones only contributed to a background radioactivity level of 26 ± 4 mHz in the neutrino analysis.

关键词： JUNO Liquid scintillator Tonpilz hydrophone Low radioactivity Sensitivity Directivity Finite element

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Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear systems Against Actuator Faults

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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International Symposium on Autonomous systems (ISAS)

作者： Shanlin Liu Yingwei Zhang Xudong Zhao Hesheng Wang College of Information Science and Engineering Northeastern University Shenyang China State Laboratory of Synthesis Automation of Process Industry Northeastern University Shenyang China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of IntelligentControl and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent.

关键词： Actuators Backstepping Fault tolerant systems Process control Artificial neural networks control systems Approximation error

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Wood Surface Discoloration Defect Detection Based on Improved HRNet

Wood Surface Discoloration Defect Detection Based on Improve...

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Chinese Automation Congress (CAC)

作者： Haoran Zhang Zhezhuang Xu Yuhang Zhu Ye Lin Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Discoloration defect detection is a significant task in the wood industry for ensuring the appearance and quality of wooden products. However, discoloration defect detection still faces some challenges due to the low contrast, blurred boundary, and slender shape of discoloration defects. To overcome these challenges, we propose a multi-branch feature encoding HRNet(MFE-HRNet) for wood surface discoloration defect detection. The network maintains high-resolution features and builds a parallel multi-resolution architecture to prevent the dilution of low-contrast discoloration defect features. Through repeated cross-fusion of multi-scale features, the network enhances the global representation of blurry features. In order to effectively preserve the detail features of the defect edge, a multi-branch feature encoding module (MFEM) is proposed as the encoding layer of the network. In the MFEM, multiple branches concurrently extract features, generating feature maps with complementary characteristics, thereby enhancing the ability of network to recognize fine details of defects. Experimental results verify the effectiveness of our method in detecting discoloration defects on wood surface.

关键词： Accuracy Automation Shape Image edge detection Wood industry Feature extraction Encoding Character recognition Faces Defect detection

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Hybrid Traffic Scheduling Based on Adaptive Time Slot Slicing in Time-Sensitive Networking

Hybrid Traffic Scheduling Based on Adaptive Time Slot Slicin...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Shouliang Wang Qimin Xu Yanzhou Zhang Lei Xu Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781728119496

The development of the Industrial Internet of Things (IIoT) is increasingly relying on the low-latency and low-jitter information exchange. Time-Sensitive Networking (TSN) guarantees the deterministic transmission by a set of mechanisms, such as Time-Aware Shaper (TAS) and Cyclic Queuing and Forwarding (CQF). However, the deterministic scheduling of hybrid traffic with different Quality of Service (QoS) is still unresolved since traffic performance is hard to meet by a single mechanism or an integrated mechanism with fixed time slot. In this paper, to realize the low-latency and low-jitter hybrid traffic scheduling, an adaptive time slot slicing (ATSS) method is proposed integrating TAS and CQF. With dynamically slicing the time slot to centralize time-triggered (TT) traffic, ATSS eliminates the irregular distribution of TT flow to support multi-hop networks. A resource calculation method based on slot occupancy judgment is devised to reduce the scheduling complexity, which raises the analysis objects into flows instead of frames. To reduce the complexity further, an incremental algorithm with the search boundary of resources is designed. The evaluation results show that ATSS reduces the jitter by 12 times and time cost by 200 times when handling over 125 mixed flows in different network topologies compared to previous work.

关键词： Adaptive scheduling Job shop scheduling Costs Network topology Quality of service Spread spectrum communication Jitter

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Robust Remote Sensing Scene Classification with Multi-view Voting and Entropy Ranking 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wang, Jinyang Wang, Tao Gan, Min Hadjichristofi, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering European University Cyprus Nicosia1516 Cyprus

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Deep convolutional neural networks have been widely used in scene classification of remotely sensed images. In this work, we propose a robust learning method for the task that is secure against partially incorrect categorization of images. Specifically, we remove and correct errors in the labels progressively by iterative multi-view voting and entropy ranking. At each time step, we first divide the training data into disjoint parts for separate training and voting. The unanimity in the voting reveals the correctness of the labels, so that we can train a strong model with only the images with unanimous votes. In addition, we adopt entropy as an effective measure for prediction uncertainty, in order to partially recover labeling errors by ranking and selection. We empirically demonstrate the superiority of the proposed method on the WHU-RS19 dataset and the AID dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Entropy

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Unsupervised Graph-Sequence Anomaly Detection for 5G Core Network control Plane Traffic 29

Unsupervised Graph-Sequence Anomaly Detection for 5G Core Ne...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Wang, Min Li, Peng Cheng, Zhang Liu, Wenmao Nie, Lei Bao, Haizhou Liu, Qin Zhang, Kai Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China Nsfocus Technologies Group Co. Ltd China Wuhan University School of Cyber Science and Engineering China

ISBN: (纸本)9798350330717

5G Core network (5GC) employs a Service Based Architecture (SBA). This architecture decomposes the control plane into multiple independent Network Functions (NFs). NFs open interfaces to provide services to other NFs, which makes the control plane more susceptible to external malicious attacks. However, existing anomaly detection methods focus more on traffic statistics features and are difficult to apply to the 5GC control plane. In this paper, we proposed GSAD, a Graph-Sequence analysis-based Anomaly Detection method for 5GC control plane traffic. We model control plane traffic as a directed graph to depict topological and NF interaction information. Further, we use the normalizing flows with temporal dependencies to mine the sequential information in the traffic. GSAD combines the topological and sequential information to provide fine-grained detection. We evaluate our proposed framework on the 5GC testbed using Free5GC and UERANSIM in various scenarios. Experimental results demonstrate that our framework outperforms the baselines significantly in terms of detection performance. © 2023 IEEE.

关键词： 5G core network anomaly detection control plane graph neural network normalizing flows

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Sparse Andrew's Sine Norm Promoting Adaptive Algorithm under Non-Gaussian Noises 5

Sparse Andrew's Sine Norm Promoting Adaptive Algorithm under...

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5th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2022

作者： Hadi, Abdul Huang, Xinqi Ali, Burhan Li, Yingsong College of Information and Communications Engineering Harbin Engineering University Harbin150001 China College of Intelligent System and Control Science Engineering Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Two sparse adaptive filtering (AF) algorithms based on Andrew's sine estimator (ASE) are presented to achieve improved performance for identifying sparse systems, where the ASE is derived within the least-square framework. Furthermore, zero-attracting (ZA) scheme is used in ASE to construct ZA-ASE and its re-weighting form (RZA-ASE) to combat non-Gaussian noises and use the sparse characteristics of the system. Their performance is investigated via simulations and compared with the least-mean square (LMS) and the maximum correntropy criterion (MCC) algorithms to show their superior performance. © 2022 IEEE.

关键词： Adaptive filtering

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A novel two-stage Dissolved Gas Analysis fault diagnosis system based semi-supervised learning

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High Voltage 2022年第4期7卷 676-691页

作者： Xuemin Tan Chao Guo Ke Wang Fu Wan College of Control Engineering Chengdu University of Information TechnologyChengduChina State Grid Chengdu Power Supply Company ChengduChina China Electric Power Research Institute BeijingChina State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina

Dissolved Gas Analysis(DGA)is an important method for oil-immersed transformer fault ***,collecting labelled DGA data is difficult because the determi-nation of the transformer fault is time-consuming and expensive in the transformer substation,but DGA data without labels is easier to ***,the paper pro-posed a semi-supervised two-stage diagnostic system based DGA by using less labelled *** two-stage system includes a novel semi-supervised feature selection based Genetic Algorithm(GA)and Support Vector Machine(SVM)model(SSL-FS-GASVM)for selecting optimal features and a novel semi-supervised transformer fault diagnosis model based improved Artificial Fish Swarm Algorithm(AFSA)and SVM(SSL-IAFSA-SVM)for optimising the SVM ***,the performances of SSL-FS-GASVM and SSL-IAFSA-SVM models are tested and compared with traditional supervised diagnostic models combined with other optimisation methods,*** results show that the proposed two-stage system works in optimising features and parameters and has strong robustness in solving small sample classification problems.

关键词： fault diagnosis system

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Comparison of Class I Flextensional Transducers with Concave Staves Machined by Different Materials

Comparison of Class I Flextensional Transducers with Concave...

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OCEANS

作者： Duo Teng Junwei Wang Zhong Luo Guolei Zhu School of Marine Science and Technology National Key Laboratory for Underwater Information Processing and Control Northwestern Polytechnical University Xi’an China

A Class I concave stave flextensional transducer is developed. Aiming at the electroacoustic performance of low frequency, high power, light weight and small size, the effect of the concave staves machined by the different materials on the flextensional transducer is studied. The finite element simulations illustrate the effect of the aluminium alloy, TC4 titanium alloy and 304 steel concave staves. The prototypes of the Class I concave stave flextensional transducer have been designed and fabricated. They have the size of Φ 96 × 160 mm, the weight of below 3 kg. Moreover, their resonance frequency is below 1.6 kHz and their source level is more than 194 dB. The tests verified the changes caused by the concave staves with different materials.

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