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检索条件"任意字段=International Conference on Pattern Analysis and Intelligence Robotics"

共 15862 条记录，以下是221-230 订阅

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Principal Components analysis Based Sticking for Drill Rod 17th

Principal Components Analysis Based Sticking for Drill Rod

引用

17th international conference on Intelligent robotics and Applications

作者： Li, Dongmin Xia, Shangfei Li, Jia Shandong Univ Sci & Technol Coll Intelligent Equipment Tai An Shandong Peoples R China Zaozhuang Tech Coll Sch Enterprise Cooperat & Res Dept Shandong Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun Jilin Peoples R China

ISBN: (纸本)9789819607792;9789819607808

It is a vital threat for coal mining to stick for drill rod during the process of drilling holes toward coal wall underground coal mines, which is prone to lead to mine accidents. To eliminate the above problem, all the factors inducing sticking for drill rod were studied. First, a complete evaluation system was built using analytic hierarchy process to reveal the interaction relationship among the factors inducing sticking for drill rod. Additionally, the characters of all the factors were analyzed and the key influencing factors were obtained using principal component analysis. Furthermore, the mechanical analysis on the drill rod was performed based on the key influencing factors, and the maximum stress region on the drill rod was obtained. Finally, the simulation results show their coincidence with the effectiveness of the key influencing factors, which proves the validity of the analysis approach on sticking for drill rod, thus the sticking for drill rod can be solved efficiently according to the analysis.

关键词： sticking for drill rod coal mine analytic hierarchy process torque principal component analysis

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Dynamic Modeling and analysis of 3-PSR Space Parallel Mechanism with Wear and Lubrication Clearance of Spherical Pair 17th

Dynamic Modeling and Analysis of 3-PSR Space Parallel Mechan...

引用

17th international conference on Intelligent robotics and Applications

作者： Yang, Yingjie Chen, Xiulong Sheng, Yongchao Shandong Univ Sci & Technol Coll Mech & Elect Engn Qingdao 266590 Peoples R China

ISBN: (纸本)9789819607853;9789819607860

To improve the dynamic response of spatial parallel mechanism caused by wear clearance and lubrication clearance, this paper takes 3-PSR mechanism as the research object, establishes the wear clearance model of spherical pair and reconstruct the wear surface. The lubrication clearance model of spherical pair is established, the Flores transition force model is introduced. Using the Lagrange multiplier method, the dynamic equations of the spatial parallel mechanism with wear clearance and the spatial parallel mechanism with lubrication clearance are derived. The effects of wear and lubrication on the dynamic characteristics of space parallel mechanism are compared and analyzed. The results demonstrate that the abrasion will accelerate the collision between clearance elements, deteriorate the dynamic performance of the mechanism. And it successfully improves the dynamic performance of the mechanism after adding lubricating oil. This paper provides theoretical support for improving the effects of wear clearance on the dynamic performance of space parallel mechanism.

关键词： Space Parallel Mechanism Dynamic Response Clearance

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BiGRU-Attention-Based Specific Emitter Identification Method 6

BiGRU-Attention-Based Specific Emitter Identification Method

引用

6th international conference on robotics, Intelligent Control and Artificial intelligence, RICAI 2024

作者： Xiong, Rongling National Key Laboratory of Complex Aviation System Simulation Chengdu610036 China The 10th Research Institute of China Electronics Technology Group Corporation Chengdu610036 China

ISBN: (纸本)9798331541699

Specific emitter identification plays a pivotal role in electronic countermeasures within the realm of complex electromagnetic environments. This study introduces a novel method for specific emitter identification, leveraging the capabilities of BiGRU-Attention (BiGRU-Att). The process begins with the segmentation of steady-state signal slices from the targeted emitter. Subsequently, six distinct features, including wavelet entropy and amplitude skewness within the time-frequency domain, are meticulously extracted. These extracted sequence feature data are then employed to train the model, culminating in the acquisition of the identification outcome. The methodology was rigorously tested using TETRA communication signals from nine distinct emitters. Experimental findings affirm that the model proficiently harnesses the timing information inherent in the specific emitter's signal, substantially enhancing the identification accuracy. This approach not only underscores the effectiveness of integrating BiGRU and attention mechanisms for signal classification but also sets a precedent for future research in specific emitter identification under intricate electromagnetic conditions. © 2024 IEEE.

关键词： Frequency domain analysis

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BSGM: Psychological State analysis Method Based on Deep Learning and Stock Market Dynamic Data Augmentation 1

引用

17th international conference on Intelligent robotics and Applications

作者： Tang, Chaoxing Liu, Chang Sun, Xiao Anhui Univ AHU IAI Joint Lab Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China

ISBN: (数字)9789819607921

ISBN: (纸本)9789819607914;9789819607921

Stock market commentary data on social media can reflect investors' state of psychology about the stock market, and investor psychoanalysis is critical to predicting the direction of the stock market. Current text sentiment analysis only mines information from social media texts and lacks the combination of real stock market data, resulting in inadequate sentiment analysis for investors. The research on text sentiment analysis tends to focus on emotions and lacks the mining of psychology, which affects the accurate grasp of investor psychology. To this end, we construct a social media dataset containing stock market data and propose an investor psychological analysis method based on deep learning and real stock market data for more accurate investor psychological judgment. We first collect 220,000 comment data based on the Weibo A-share topic, then deduplicate, clean, and psychologically label the data. Subsequently, we collect stock market data over the comment period and construct the Psy-Stock dataset. Finally, we propose a method to analyze the psychological state of investors by fusing stock market data with textual features extracted from a pre-trained model, named BERT-Stock United Learning. After extensive experimental validation, our proposed methodology shows significant advantages over multiple baseline models in investor psychology analysis.

关键词： Deep Learning Sentiment analysis Financial Psychology

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Neural Network-Based Prediction Intervals for Uncertainty Quantification in VI Curves 7

Neural Network-Based Prediction Intervals for Uncertainty Qu...

引用

7th international conference on pattern Recognition and Artificial intelligence, PRAI 2024

作者： Zhao, Zheng Lin, Peng Artificial Intelligence Institute Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9798350350890

The voltage-current (VI) curve often exhibits uncertainties due to component tolerances and variations in measurement techniques, potentially compromising fault analysis accuracy. This paper proposed a fault analysis approach utilizing neural network-based prediction intervals to quantify uncertainty in circuit board VI curves. Firstly, a neural network is designed to precisely model the VI curves, employing a mean squared error loss function to ensure the model's fidelity in capturing the curves' distinctive features. Secondly, statistical methods are applied to estimate the uncertainty of the modeled VI curves, depicting the fluctuation range of the VI curve. For the fault analysis phase, two critical indicators: coverage rate and curve similarity are incorporated to enhance the fault detection process. Finally, simulation experiments conducted on experimental circuit boards confirm the efficacy of the proposed approach. © 2024 IEEE.

关键词： Prediction models

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Exploring Service Attributes of Hotel Robots from the Perspective of Customer Experience: An Emotional analysis Based on Customer Online Reviews 3

Exploring Service Attributes of Hotel Robots from the Perspe...

引用

3rd international conference on Service robotics, ICoSR 2024

作者： Sun, Minghao Zhang, Yan School of Tourism and Urban-Rural Planning Zhejiang Gongshang University Hangzhou China

ISBN: (纸本)9798350367119

Existing research on hotel robot services lacks an in-depth analysis of service attributes and customers' emotional needs from online reviews. This paper aims to address this gap by exploring hotel robot service attributes and revealing customer satisfaction and emotional tendencies towards them. We conducted a detailed analysis of the collected customer reviews using sentiment analysis tools, including text mining, feature word extraction, sentiment dictionary construction, and sentiment scoring methods. The results indicate that customers generally express satisfaction with the entertainment interaction, intelligent reception, and delivery services of hotel robots, believing that these attributes enhance their accommodation experience. However, some customers express concerns or dissatisfaction regarding the intelligence level, fault handling ability, and interpersonal communication skills of robots. Finally, this study provides specific directions for robot suppliers and hotel managers to improve robot services, offering both theoretical and practical insights for the application of service robots in hotels. © 2024 IEEE.

关键词： Sales

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Artificial intelligence Enabled Cognitive Computing for AlN/β-Ga2O3 HEMT 2

Artificial Intelligence Enabled Cognitive Computing for AlN/...

引用

2nd international conference on Computational intelligence, Communication Technology and Networking, CICTN 2025

作者： Verma, Yogesh Kumar Bhatia, Kamal Khushboo Bhanot, Aastha Roy, Trijeeta Sinha, Supriya Laksh School of Electronics and Electrical Engineering Lovely Professional University Jalandhar India School of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9798331530389

HEMT devices have potential to handle the fast processing in applications such as real-time diagnostics utilizing the artificial-intelligence (AI) enhanced abilities. Along-with this HEMT devices can be extensively used in radar and defense systems to obtain faster response time and threat detection. This integration of AI and cognitive computing enables pattern-recognition and adaptive learning. The cognitive computing techniques integrated with HEMT enables the real-time analysis and predictions. For precise sensor data the amplification should be done at either no or very less noise levels. © 2025 IEEE.

关键词： Gallium compounds

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Dual Attention Based Multi-scale Feature Fusion Network for Indoor RGBD Semantic Segmentation 26

Dual Attention Based Multi-scale Feature Fusion Network for ...

引用

26th international conference on pattern Recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Hua, Zhongwei Qi, Lizhe Du, Daming Jiang, Wenxuan Sun, Yunquan Fudan Univ Acad Engn & Technol Shanghai Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

RGBD semantic segmentation combined with color image information and depth information can effectively alleviate the problems of low classification accuracy and difficulty in accurately dividing edges between different semantic regions in indoor scenes caused by complex backgrounds, uneven lighting, similar object textures, spatial overlap, and occlusion. To fully fuse the color features with the spatial position and hierarchical information of objects, this paper proposes a multi-scale network model based on the dual attention mechanism (channel attention and spatial attention)(DAMFNet), which effectively integrates color texture features and spatial structure features, and further improves the semantic segmentation performance of indoor objects. We evaluate the proposed network model on the common indoor dataset SUNRGBD and achieve state-of-the-art results. In addition, this paper also demonstrates the excellent segmentation accuracy and effect of the proposed network model on self-built indoor datasets and in real-world application scenarios.

关键词： Image color analysis Fuses Semantic segmentation Image edge detection Semantics Lighting Color

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Fiber Optic Vibration Signal Recognition Based on GOA-VMD and Weighted Reconstruction 6

Fiber Optic Vibration Signal Recognition Based on GOA-VMD an...

引用

6th international conference on robotics, Intelligent Control and Artificial intelligence, RICAI 2024

作者： Feng, Xinyu Mei, Kangjian Xie, Yingjuan Xu, Haiyan College of Information Science and Engineering Hohai University Changzhou China

ISBN: (纸本)9798331541699

In distributed fiber optic vibration sensing systems, the fiber optic vibration signals are often affected by complex real-world environments, resulting in unstable signal recognition outcomes. To address the question, this paper proposes a recognition method combining the Goose Optimization Algorithm (GOA), Variational Mode Decomposition (VMD), and Support Vector Machines (SVM). First, GOA is used to optimize VMD, enabling the adaptive selection of VMD parameters to obtain more stable Intrinsic Mode Functions (IMFs). Next, correlation coefficients are used to screen the IMFs, retaining the highly correlated IMFs, which are then weighted and combined to reconstruct the signal. Time-domain and frequency-domain features are then extracted from the reconstructed vibration signal. Finally, SVM is employed to recognize signals, and GOA is also used to optimize the parameters in SVM to enhance its performance. Experimental results show that the method based on GOA-VMD and weighted reconstruction achieves higher accuracy, verifying the superiority compared with other recognition methods. © 2024 IEEE.

关键词： Frequency domain analysis

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Spatio-temporal analysis of power demand in smart grid based on stacked autoencoder 10

Spatio-temporal analysis of power demand in smart grid based...

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10th international conference on Energy Materials and Electrical Engineering, ICEMEE 2024

作者： Li, Hai Feng, Jianfeng Gu, Huangjing Xue, Xiangyang Yan, Nannan Cao, Yu Lv, Haosheng Wang, Xiaodi Yang, Tianyu State Grid Shanghai Electric Power Company Shanghai200122 China School of Big Data Institute of Brain-Like Intelligence Science and Technology Fudan University Shanghai200433 China Institute of Brain and Intelligence Science and Technology School of Computer Science Fudan University Shanghai200433 China

ISBN: (纸本)9781510686243

Energy demand in modern cities is becoming more and more complex. Knowing its pattern can help managers make proper decisions. However, finding the pattern of large-scale spatial coverage fields, such as a whole city, is not easy. In this paper, a data-driven stacked autoencoder-based framework is proposed to capture the spatio-temporal pattern for electricity consumption. The data can be organized as a one-time series and the geography coverage of the analysis can be determined by the collected data. An experiment with collected data proves the feasibility of the framework. © 2024 SPIE.

关键词： Time series

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