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12th International Conference on Intelligent control and information Processing, ICICIP 2024

作者： Tang, Piqi Xing, Jun Wang, Xinzhe Fan, Jianchao School of Information Science and Engineering Dalian Polytechnic University Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350308020

Industrial safety has become a topic of great concern, especially in the oil and gas industry, where employee health and safety are of paramount importance. The correct wearing of work clothes and helmets is vital to employee safety, but there are still employees who do not comply. To solve this problem, many companies have adopted manual supervision and strengthened safety education, but the results are not ideal. Therefore, researchers developed an automatic helmet detection system. The development of target detection algorithms has gone through the era of manual feature construction and deep learning, in which single-stage target detection algorithms perform well in terms of speed and performance. However, existing methods are difficult to balance between detection efficiency and accuracy. This research aims to develop an all-weather real-time safety helmet and work clothes monitoring system, using automatic data collection methods to complete the collection of on-site data, saving a lot of manual screening time. The produced data set contains scenes in various time periods, and then the YOLOv7 network is used to train the data set, and Tensorrt is used to optimize the model. Experimental results show that the YOLOv7-based safety helmet and work clothes detection method MAP@O.5:0.95 has an accuracy of 80.42% and a YOLOv7 + Tensorrt detection speed of 90.39 FPS. YOLOv3 and YOLOv5 were used as comparative experiments to prove that our proposed helmet and work clothes detection method can meet the requirements of real-time detection environment while also having high detection accuracy. © 2024 IEEE.

关键词： Accident prevention

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Multisource Remote Sensing Fusion for Marine Aquaculture information Extraction 16

Multisource Remote Sensing Fusion for Marine Aquaculture Inf...

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16th International Conference on Advanced Computational Intelligence, ICACI 2024

作者： Wang, Zhuo Dong, Jie Wang, Xinzhe Fan, Jianchao Dalian Polytechnic University School of Information Science and Engineering Dalian China Dalian University of Technology School of Control Science and Engineering Dalian China

ISBN: (纸本)9798350308501

Floating raft aquaculture is an important part of mariculture, and the use of single source data is easy to inhibits the extraction of floating raft aquaculture, so the advantages of integrating multisource data are particularly important. However, multisource remote sensing fusion still has the problem of data information imbalance and feature redundancy. In this paper, a channel exchanging bottleneck attention network (CEBANet) is proposed to extract features from multisource remote sensing data, determine whether features are redundant by using the scaling factor of batch normalization (BN) layer, replace the current redundant features with another modal feature. Bottleneck attention module (BAM) and feature calibration module are added to improve the capability of feature extraction and receptive field processing. The CEBANet is optimized by the sparse constraint on channel exchanging condition and cross-entropy loss and consistency loss. Using GF-5 and GF-3 data from Jinzhou District of Dalian City, it is proved that the proposed model can realize the complementary advantages of multisource remote sensing data and improve the accuracy of extracting floating raft aquaculture areas. © 2024 IEEE.

关键词： Remote sensing

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Application of Medical Image Detection technology Based on Deep Learning in Pneumoconiosis Diagnosis

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Data Intelligence 2023年第4期5卷 1033-1047页

作者： Shengguang Peng School of Engineering and Management Pingxiang UniversityPingxiang 337055JiangxiChina School of Information and Control Engineering China University of Mining and TechnologyXuzhou 22100oJiangsuChina

Pneumoconiosis is a disease characterized by pulmonary tissue deposition caused by dust exposure in the *** China,due to the large number and wide distribution of pneumoconiosis patients,there is a high demand for the case data of lung biopsy during the diagnosis of *** text studied the application of medical image detection technology in pneumoconiosis diagnosis based on deep learning(DL).A medical image detection and convolution neural network(CNN)based on DL was analyzed,and the application of DL medical image technology in pneumoconiosis diagnosis was *** experimental results in this paper showed that in the last round of testing,the accuracy of ResNet model including deconvolution structure reached 95.2%.The area under curve(AUC)value of the working characteristics of the subject is *** sensitivity was 99.66%,and the specificity was 88.61%.The non staging diagnosis of pneumoconiosis improved the diagnostic sensitivity while ensuring high *** the same time,Delong test method was used to conduct AUC analysis on the three models,and the results showed that model C was more effective than model A and model *** is no significant difference between model A and model B,and there is no significant difference in diagnostic *** a word,the diagnosis of the model has high sensitivity and low probability of missed diagnosis,which can greatly reduce the working pressure of diagnostic doctors and effectively improve the efficiency of diagnosis.

关键词： Pneumoconiosis Diagnosis Deep Learning Medical Image Detection Lung Imaging Convolutional Neural Network

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A Multi-scale Spatial Attention Network for Aquaculture Extraction Based on Time Series SAR Image

A Multi-scale Spatial Attention Network for Aquaculture Extr...

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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Wang, Pingzhuo Wang, Xinzhe Fan, Jianchao School of Information Science and Engineering Dalian Polytechnic University Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350384185

Synthetic aperture radar (SAR) is a kind of radar system with high resolution remote sensing imaging capability, which can be used to obtain remote sensing images at different times and under complex weather *** this study, a multi-scale spatial attention network based on time series aquaculture extraction (MSATUnet) is *** model uses multiple time series SAR images to extract sequence features, and uses a encoder decoder network design to extract spatial features of different *** the encoder part, the network calculates the spatial attention weight of the multi-scale feature map to improve the feature expression ability of the convolutional layer for the *** the design of convolutional long short-term memory (ConvLSTM) and self-attention mechanism in the deep layer of the network, the network can capture the temporal features of sequence images and improve the ability of global semantic information of the *** addition, in order to solve the problem of target adhesion, a edge loss function is introduced to adjust the edge *** experiment uses the time-series images taken by GF-3 satellite as the data input, and compares with other *** results show that MSATUnet outperforms traditional single-input networks and multiple time series models, which verifies the effectiveness of multiple time series in marine aquaculture extraction. © 2024 IEEE.

关键词： Aquaculture

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Intelligent Orchestrating of IoT Microservices Based on Reinforcement Learning

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Chinese Journal of Electronics 2022年第5期31卷 930-937页

作者： WU Yuqin SHEN Congqi CHEN Shuhan WU Chunming LI Shunbin WEI Ruan College of Computer Science and Technology Zhejiang University College of Information and Electrical Engineering Ningde Normal University Zhejiang Lab College of Control Science and Engineering Zhejiang University

With the recent increase in the number of Internet of things(IoT) services, an intelligent scheduling strategy is needed to manage these services. In this paper, the problem of automatic choreography of microservices in IoT is explored. A type of reinforcement learning(RL) algorithm called TD3 is used to generate the optimal choreography policy under the framework of a softwaredefined network. The optimal policy is gradually reached during the learning procedure to achieve the goal, despite the dynamic characteristics of the network environment. The simulation results show that compared with other methods, the TD3 algorithm converges faster after a certain number of iterations, and it performs better than other non-RL algorithms by obtaining the highest reward. The TD3 algorithm can effciently adjust the traffic transmission path and provide qualified IoT services.

关键词： software-defined network telecommunication scheduling intelligent orchestrating nonRL algorithms reinforcement learning software defined networking optimal choreography policy reinforcement learning algorithm Internet of things services network environment IoT microservices TD3 algorithm traffic transmission path Internet of Things qualified IoT services intelligent scheduling strategy dynamic characteristics

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Numerical and experimental studies on the effectiveness of time-varying electromagnetic fields in reducing electron density

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Plasma Science and technology 2023年第6期25卷 30-40页

作者：郭韶帅谢楷徐晗付麦霞牛营营 Key Laboratory of Grain Information Processing and Control(Henan University of Technology) Ministry of EducationZhengzhou 450001People's Republic of China College of Information Science and Engineering Henan University of TechnologyZhengzhou 450001People's Republic of China School of Aerospace Science and Technology Xidian UniversityXi'an 710071People's Republic of China

When an aircraft or a hypersonic vehicle re-enters the atmosphere,the plasma sheath generated can severely attenuate electromagnetic wave signals,causing the problem of communication blackout.A new method based on time-varying E×B fields is proposed to improve on the existing static E×B fields and mitigate the radio blackout *** use of the existing method is limited by the invalid electron density reduction resulting from current density j=0 A m^(-2)in plasma beyond the Debye *** most remarkable feature is the introduction of a time-varying electric field to increase the current density in the plasma to overcome the Debye shielding effect on static electric ***,a magnetic field with the same frequency and phase as the electric field is applied to ensure that the electromagnetic force is always acting on the plasma in one *** order to investigate the effect of time-varying E×B fields on the plasma electron density distribution,two directions of voltage application are considered in numerical *** simulation results indicate that different voltage application methods generate electromagnetic forces in different directions in the plasma,resulting in repulsion and vortex effects in the plasma.A comparison of the vortex effect and repulsion effect reveals that the vortex effect is better at reducing the electron *** local plasma electron density can be reduced by more than 80%through the vortex effect,and the dimensions of the area of reduced electron density reach approximately 6 cm×4 cm,meeting the requirements of electromagnetic wave ***,the vortex effect of reducing the electron density in RAM-C(radio attenuation measurements for the study of communication blackout)reentry at an altitude of 40 km is *** the basis of the simulation results,an experiment based on a rectangular-window discharge device is proposed to demonstrate the effectiveness of the vortex *** results show t

关键词： blackout magnetic window method time-varying E×B fields

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Chinese Nested Entity Recognition Benchmark in the Field of Naval Warfare

Chinese Nested Entity Recognition Benchmark in the Field of ...

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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Zhang, Shengjie Li, Qi Kou, Yueping Wang, Jun Li, Zhengwei Wu, Baolei School of Computer Science and Technology China University of Mining and Technology Xuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China

ISBN: (纸本)9798350384185

Named entity recognition (NER) is a fundamental task in natural language processing and a key technology for building knowledge graphs. However, the performance of NER is often limited by domain-specific characteristics. For unpopular Chinese entity recognition fields, such as naval warfare, NER typically performs poorly due to the lack of sufficient pre-trained models and labeled data. Furthermore, the naval warfare field includes numerous specialized terms and complex domain knowledge, with nested structures and physical relationships that are uncommon in conventional corpora, posing additional challenges for NER. To address these issues, we manually construct a high-quality naval warfare domain corpus. We also propose a new nested entity recognition framework, which fully leverages entity boundary information, enhances the learning of entity adjacency modeling information, and uses multi-scale convolution to obtain richer span feature representation. Experiments demonstrate the effectiveness of this method in four Chinese NER corpora. © 2024 IEEE.

关键词： Naval warfare

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Channel Estimation for Intelligent Reflecting Surface 6

Channel Estimation for Intelligent Reflecting Surface

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6th IEEE International Conference on Electronic information and Communication technology, ICEICT 2023

作者： Chen, Shuo Zu, Zhengquan Li, Zixi Fu, Xiangyu Li, Kui Li, Yansheng School of Information and Control Engineering Qingdao University of Technology Qingdao China Qingdao Inte Information Technology Co. Ltd Qingdao China

ISBN: (纸本)9798350399059

With the popularity of 5G technology, more and more scholars have invested in the research of 6G technology with the advantages of higher speed, high precision and low delay. However, 6G technology has the disadvantages of high power consumption and high hardware cost, and the intelligent reflecting surface is a plane composed of a large number of low-cost passive components with low energy consumption, so IRS has a very broad application prospect in the future auxiliary wireless communication. So we can use intelligent reflecting surface technology in information transmission to reduce power consumption. In this paper, we introduce a random number seed to imitate the noise in real life, an intelligent reflecting surface (IRS) to assist the communication between the transmitter and single antenna users, and optimize the power by optimizing the distance and angle. The final results show that under the condition of the same bit error rate, the power consumption is the lowest with the assistance of an intelligent reflecting surface (IRS) at a long distance. © 2023 IEEE.

关键词： Electric power utilization

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control of Unmanned Bicycle Based on Tensor Product Model Transformation and Hammersley Sampling Method 12

Control of Unmanned Bicycle Based on Tensor Product Model Tr...

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12th International Conference on Intelligent control and information Processing, ICICIP 2024

作者： Xie, Depeng Wang, Degang Zhao, Guoliang Li, Hongxing School of Control Science and Engineering Dalian University of Technology Dalian China College of Electronic Information Engineering Inner Mongolia University Hohhot China

ISBN: (纸本)9798350308020

In this paper, path planning and balance control of unmanned bicycles are investigated. Firstly, the Deep Deterministic Policy Gradient (DDPG) algorithm is used to train the Artificial Potential Field (APF) algorithm to implement path planning in complex maps. Secondly, a dual closed-loop control strategy is adopted for the path tracking and the balance control. In the inner-loop control, the Hammersley sampling (HS) method is used to obtain the hyper-cube network model of the unmanned bicycle, and the balance controller is designed based on the tensor product (TP) model transformation and parallel distributed compensation (PDC) method for stabilization of the unmanned bicycle. The outer-loop control system uses the Ackermann steering geometry model to track the planned path. Finally, the simulation results demonstrate that the path planning algorithm plans the path that satisfies the constraints and the designed balance controller can keep the unmanned bicycle balanced well during trajectory tracking. © 2024 IEEE.

关键词： Motion planning

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Resilient Event-Triggered control of Connected Automated Vehicles Under Cyber Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2300-2302页

作者： Ning Zhao Xudong Zhao Ning Xu Liang Zhang the College of Control Science and Engineering Bohai UniversityJinzhou 121013China the Institute of Ocean Research Bohai UniversityJinzhou 121013China the Faculty of Electronic Information and Electrical Engineering Dalian University of TechnologyDalian 116024China the College of Information Science and Technology Bohai UniversityJinzhou 121013China

Dear Editor,This letter contributes to designing a resilient event-triggered controller for connected automated vehicles under cyber attacks,including denial-of-service(DoS)and deception *** characterize the effect of... 详细信息

关键词： DoS service automated

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