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检索条件"机构=Information and Control Engineering Technology"

共 17840 条记录，以下是401-410 订阅

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Visual Perception Challenges in Adverse Weather for Autonomous Vehicles: A Review of Rain and Fog Impacts 7

Visual Perception Challenges in Adverse Weather for Autonomo...

引用

7th IEEE information technology, Networking, Electronic and Automation control Conference, ITNEC 2024

作者： Qiu, Yongsheng Lu, Yuanyao Wang, Yuantao Yang, Chaochao School of Electrical and Control Engineering North China University of Technology Beijing China School of Information Science and Technology North China University of Technology Beijing China

ISBN: (纸本)9798350370782

Autonomous driving systems (ADS) offer efficient solutions for transportation, but their visual perception capabilities are significantly affected under adverse weather conditions such as rain and fog, posing a threat to vehicle safety. Rain droplets and fog can interfere with sensors, leading to inaccurate information acquisition and increased driving risks. Therefore, enhancing the environmental perception of ADS in adverse weather conditions is a critical research focus. This paper systematically evaluates the impacts and challenges of adverse weather conditions, specifically rain and fog, on ADS environmental perception sensors. It explores the imaging characteristics of rain and fog and their effects on visual perception, summarizing dehazing and deraining algorithms. Ad-ditionally' this paper reviews the development status of advanced visual tasks such as object detection in autonomous driving under adverse weather conditions. The aim of this paper is to provide a comprehensive understanding of the reliability of ADS in adverse weather conditions, laying a foundation for future de-velopments and practical applications in autonomous driving technology. © 2024 IEEE.

关键词： Rain

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Balancing Accuracy and Beyond-Accuracy Indicators in Course Recommendation through Multi-Objective Reinforcement Learning 4

Balancing Accuracy and Beyond-Accuracy Indicators in Course ...

引用

4th IEEE International Conference on information technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Song, Wei Guo, Yu Ning, Keqing Guo, Wei North China University of Technology School of Information Science and Technology Beijing China North China University of Technology School of Electrical and Control Engineering Beijing China

ISBN: (纸本)9798350363586

It is crucial to balance accuracy and beyond-accuracy indicators in recommendations through multi-objective optimization (MOO) methods. Existing multi-objective recommendation methods have several issues: they cannot dynamically model users' long-term and short-term preferences;they fail to solve non-convex problems and are prone to getting stuck in local optima;they neglect the issues of diversity and novelty in recommendations, etc. Therefore, this paper proposes using multi-objective reinforcement learning (MORL) to address the trade-off between accuracy, diversity, and novelty in course recommendation. First, dynamic modeling of users' long-term and short-term interest preferences is conducted using user feature information, course feature information, and users' historical course features. Second, the Actor-Critic network is applied to course recommendation, and a multi-layer reward function and Q-value function are introduced to learn multiple objectives simultaneously. Diversity and novelty assessment indicators are also added to measure the effectiveness of course recommendations Finally, experiments are conducted on three real MOOC datasets, proving the superior performance of the proposed course recommendation model in dynamically modeling user preferences and balancing accuracy and non-accuracy aspects. © 2024 IEEE.

关键词： Contrastive Learning

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Fine-Grained Recognition Framework For Ship Targets In SAR Images Based On Component Architecture 2

Fine-Grained Recognition Framework For Ship Targets In SAR I...

引用

2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Pan, Yongchen Su, Can Zeng, Hongcheng Wang, Yamin Yang, Wei Li, Chunsheng Yu, Jiaqi Li, Dongliang Beihang University School of Electronic Information Engineering Beijing China Beijing Institute of Control and Electronics Technology Beijing China

ISBN: (纸本)9798331515669

Applying deep learning network models in SAR image processing has significantly improved speed of target recognition performance. Nevertheless, due to the SAR imaging principle distinct from optical images, these models struggle to distinguish strong scattering characteristics from clutter, resulting in a high false alarm rate in applications of target recognition. To ensure correctness practically, the expert analysis for discern various structures is strongly necessary for fine-grained recognition after possessing through integrating intelligent models, with extra time and manpower cost. This study introduces a fine-grained recognition framework for ship targets in SAR images based on component architecture. A ship targets dataset from Gaofen-3 SAR images, with three classified ship types is designed to train the model, including meticulous analysis and labeling for target categorization, axis orientation, and key component localization. Features of different dimensions extracted via pre-trained ResNet-50 to construct a weight dossier, and a multi-task joint detection mode are embedded into the framework to integrate the experts' ship recognition method built on component architecture into the intelligent network model. This yields an accuracy rate surpassing 89% for ship identification, eclipsing conventional intelligence models by a significant margin. The framework successfully bolsters the ability of target awareness affiliated with SAR ocean monitoring. © 2024 IEEE.

关键词： Radar imaging

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Unsupervised Feature Fusion Model for Marine Raft Aquaculture Sematic Segmentation Based on SAR Images 14

Unsupervised Feature Fusion Model for Marine Raft Aquacultur...

引用

14th International Conference on information Science and technology, ICIST 2024

作者： Li, Mengmeng 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)9798350353334

Marine aquaculture semantic segmentation provides a scientific basis for marine regulation and plays an important role in marine ecological protection and management. Currently, most high-performance marine aquaculture segmentation networks are trained by supervised learning. This approach requires collecting a large number of accurate manually labelled samples for training, but the labelled samples are difficult to obtain. To solve this problem, this paper proposes an unsupervised feature fusion model (UFFM) for marine raft aquaculture semantic segmentation. Firstly, a pseudo-label generator is designed to label the training samples, and a coarse mask is generated using saliency feature clustering. The training samples with pseudo-labels are inputted into a multilevel feature fusion module to extract further and continuously improve the graphical shapes and categories of the objects under the guidance of cross-entropy loss. The pseudo-labels are optimised under continuous iteration to improve the model segmentation performance. Comparison experiments on the GF-3 dataset demonstrate the effectiveness of UFFM. © 2024 IEEE.

关键词： Semantic Segmentation

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Modeling of High Gain Tunneling Diode using Verilog-A for Backscatter Antenna 6

Modeling of High Gain Tunneling Diode using Verilog-A for Ba...

引用

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

作者： Li, Zixi Chen, Shuo Zu, Zhengquan 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

Based on the negative resistance characteristics of tunnel diode, a new low-power backscatter signal transmitter is designed to increase the reflected echo. By constructing the equivalent circuit of tunnel diode AI301A and adjusting different voltage values, we measured the reflection gain at each voltage. The experimental results show that the maximum reflection gain is 9.704dB at the voltage of 120mV and the frequency of 2.8 GHz. © 2023 IEEE.

关键词： Equivalent circuits

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Multi-Channel Selective Light Transmission Based on Hybrid Topological Photonic Crystals

Multi-Channel Selective Light Transmission Based on Hybrid T...

引用

2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024

作者： Liu, Jie Zhang, Xiao Liang, Li Li, Feifei Poo, Yin School of Electronic Science and Engineering Nanjing University Nanjing China School of Information and Control Engineering China University of Mining and Technology Xuzhou China

ISBN: (纸本)9798350383317

We observed the coexistence of quantum anomalous Hall and valley Hall phases in the same photonic band gap. Under the broken time-reversal and parity symmetries, the phase transition between the two topological phases can be flexibly manipulated. Relying on the bulk-edge correspondence, we built a pho-tonic waveguide with various boundary configurations that functioned with multi-channel selective light transmission. © 2024 IEEE.

关键词： Light transmission

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IEARACDA: Predicting circRNA-disease associations based on information enhancement and arctangent rank approximation

IEARACDA: Predicting circRNA-disease associations based on i...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Song, Zheqi Liu, Tiyao Wu, Wenhao Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Circular RNAs (circRNAs) are non-coding RNA molecules that play a significant role in cell regulation and disease occurrence. In recent years, the use of computational methods to predict circRNAs associated with diseases has become a research focus. However, these methods are often constrained by the noise interference and an insufficient exploration of the underlying information in known associations. Therefore, this paper proposes an information enhancement and arctangent rank approximation based method to predict potential circRNA-disease associations (IEARACDA). Firstly, information enhancement methods are used to make circRNA and disease similarity information more complete and to reduce the sparsity of known association. Subsequently, the arctangent rank approximation method is employed to mitigate the potential bias issues that may arise from the nuclear norm, and optimization is carried out using the inexact augmented Lagrange multiplier (IALM) method. Finally, potential associations are predicted through heterogeneous graph inference method. The experimental results demonstrate that IEARACDA exhibits superior performance compared to existing methods. Furthermore, case studies for hepatocellular carcinoma and lung cancer provide additional confirmation of the accuracy and practical significance of the model predictions. © 2024 IEEE.

关键词： Lung cancer

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Chinese Named Entity Recognition with Integrated Channel Attention 5

Chinese Named Entity Recognition with Integrated Channel Att...

引用

5th International Conference on Artificial Intelligence and Computer engineering, ICAICE 2024

作者： Song, Wei Zheng, He Guo, Wei Ning, Keqing School of Information Science and Technology North China University of Technology Beijing China School of Electrical and Control Engineering North China University of Technology Beijing China

ISBN: (纸本)9798331528911

Using an Iterated Dilated Convolutional Neural Network (IDCNN) in Chinese Named Entity Recognition (NER) helps capture local information. However, the contribution of information from different positions in a sentence to the judgment of the current position is not uniform, which needs to be considered when expanding a Dilated Convolutional Neural Network (DCNN). To address this, this paper incorporates Efficient Channel Attention (ECA) into DCNN and constructs the It-erated Dilated Convolutional Attention Network (IDCAN). The model focuses on the importance of different channel information and integrates the Linguistically-motivated Bidirectional Encoder Representation from Transformers (LERT), Bidirectional Gated Recurrent Units (BiGRU), and Conditional Random Fields (CRF) to form a Chinese NER model. The model uses LERT for word vector construction, BiGRU for capturing temporal features, IDCAN for extracting key local features, and CRF for feature decoding. On the Daily, MSRA and E-commerce Chinese datasets, the model achieved F1 scores of 96.88%, 95.15% and 90.52%, respectively, outperforming other models. © 2024 IEEE.

关键词： Convolutional neural networks

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Real-time Detection of Safety Helmet and Workwear Based on YOLOv7 12

Real-time Detection of Safety Helmet and Workwear Based on Y...

引用

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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Exponential and Finite-Time Consensus of Multiagent Systems Via Curve Suppression Method

Exponential and Finite-Time Consensus of Multiagent Systems ...

引用

2024 China Automation Congress, CAC 2024

作者： Hu, Jiazhong Zhou, Yufeng Chai, Li College of Information Science and Engineering Wuhan University of Science and Technology Wuhan China College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

This article investigates the exponential and finite-time leader-following consensus of first and second order multiagent systems (MASs). Unlike the finite-time Lyapunov method, the curve suppression method based on the exponential and linear suppression functions is respectively designed to achieve the exponential and finite-time leader-following consensus of MASs. Especially, the distributed controllers are designed to reduce communication between MASs, where does not require the design of complex fractional-order controllers to achieve finite-time consensus. Furthermore, the first-order consensus is extended to the second-order consensus by defining a new system state including position and velocity. Finally, the effectiveness of the suggested approach is verified through numerical simulations. © 2024 IEEE.

关键词： Lyapunov methods

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