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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Li, Qiuting Wang, Xiuqing Yang, Yunpeng Wang, Ruiyi Lv, Feng Hebei Normal University College of Computer and Cyber Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China

ISBN: (纸本)9781665401159

In the era of big data, it is of great significance to study data-driven fault diagnostic methods for equipment in complex industrial environments. In this paper, a data-driven fault diagnostic method for rolling bearing based on RF-LSTM is proposed, which combines random forest (RF) algorithm with long and short term memory (LSTM) networks. RF is used to select sensor data's features, which have great influence on rolling bearing fault diagnosis, and LSTM is used for prediction rolling bearing's working states. The proposed method is used for fault diagnosis of the Case Western Reserve University (CWRU) bearing dataset and the experimental results validated the effectiveness of the proposed RF-LSTM model. Experimental results also show that the performance of the RFLSTM model is better than that of RF, LSTM, RF-RF, PCA-RF, PCA-LSTM models. © 2021 IEEE.

关键词： Failure analysis

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Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension

arXiv

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arXiv 2023年

作者： Cao, Tingfeng Wang, Chengyu Tan, Chuanqi Huang, Jun Zhu, Jinhui School of Software Engineering South China University of Technology China Alibaba Group China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

In cross-lingual language understanding, machine translation is often utilized to enhance the transferability of models across languages, either by translating the training data from the source language to the target, or from the target to the source to aid inference. However, in cross-lingual machine reading comprehension (MRC), it is difficult to perform a deep level of assistance to enhance cross-lingual transfer because of the variation of answer span positions in different languages. In this paper, we propose X-STA, a new approach for cross-lingual MRC. Specifically, we leverage an attentive teacher to subtly transfer the answer spans of the source language to the answer output space of the target. A Gradient-Disentangled Knowledge Sharing technique is proposed as an improved cross-attention block. In addition, we force the model to learn semantic alignments from multiple granularities and calibrate the model outputs with teacher guidance to enhance cross-lingual transferability. Experiments on three multi-lingual MRC datasets show the effectiveness of our method, outperforming state-of-the-art approaches. 1 Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Keeping Pace with Ever-Increasing data: Towards Continual Learning of Code Intelligence Models

Keeping Pace with Ever-Increasing Data: Towards Continual Le...

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International Conference on Software engineering (ICSE)

作者： Shuzheng Gao Hongyu Zhang Cuiyun Gao Chaozheng Wang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory and Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Big Data and Software Engineering Chongqing University China

Previous research on code intelligence usually trains a deep learning model on a fixed dataset in an offline manner. However, in real-world scenarios, new code repositories emerge incessantly, and the carried new knowledge is beneficial for providing up-to-date code intelligence services to developers. In this paper, we aim at the following problem: How to enable code intelligence models to continually learn from ever-increasing data? One major challenge here is catastrophic forgetting, meaning that the model can easily forget knowledge learned from previous datasets when learning from the new dataset. To tackle this challenge, we propose REPEAT, a novel method for continual learning of code intelligence models. Specifically, REPEAT addresses the catastrophic forgetting problem with representative exemplars replay and adaptive parameter regularization. The representative exemplars replay component selects informative and diverse exemplars in each dataset and uses them to re-train model periodically. The adaptive parameter regularization component recognizes important parameters in the model and adaptively penalizes their changes to preserve the knowledge learned before. We evaluate the proposed approach on three code intelligence tasks including code summarization, software vulnerability detection, and code clone detection. Extensive experiments demonstrate that REPEAT consistently outperforms baseline methods on all tasks. For example, REPEAT improves the conventional fine-tuning method by 1.22, 5.61, and 1.72 on code summarization, vulnerability detection and clone detection, respectively.

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Impact of a Compound Droplet on a Solid Surface: The Effect of the Shell on the Core

SSRN

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SSRN 2024年

作者： Du, Yuying Dai, Li Qian, Lijuan Zhou, Fang Ma, Yuechao College of Mechanical and Electrical Engineering China Ji liang University Hangzhou310018 China Key Laboratory of intelligent Manufacturing Quality Big Data Tracing and Application of Zhejiang Province China Ji Liang University Hangzhou310018 China

The dynamic behavior of compound droplets impacting a solid surface was studied via experiments over κ (defined as the ratio of the compound droplet shell thickness h to the diameter D0 of compound droplet) ranging from 0 to 0.34, We ranging from 25 to 325 and the viscosity of the shell ranging from 5.92mPa·s to 21.1mPa·s. The spreading diameter ratio, the maximum spreading dynamic contact angle and spreading speed of the core were investigated. Four modalities of the core of compound droplets were observed on the solid surface, including a) core rebound, b) no rebound, c) core rebound splitting, d) core splitting. The results revealed that the thickness of the shell, We, and the viscosity of the shell have a significant effect. The high viscosity oil shell is conducive to its spreading. As the thickness of the oil shell increases, its cushioning effect on the water core also increases. In addition, the boundary between four modalities of the core was identified as a result of the quantified relation between modalities and the shell thickness on the core. Two equations were also proposed for predicting the maximum spreading diameter ratio of the core, which include dimensionless parameters of the compound droplets. © 2024, The Authors. All rights reserved.

关键词： Viscosity

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Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension 39

Hierarchical Alignment-enhanced Adaptive Grounding Network f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yaxian Ding, Henghui He, Shuting Jiang, Xudong Wei, Bifan Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University China Institute of Big Data Fudan University China Shanghai University of Finance and Economics China Nanyang Technological University Singapore School of Continuing Education Xi'an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University China

ISBN: (纸本)157735897X

In this work, we address the challenging task of Generalized Referring Expression Comprehension (GREC). Compared to the classic Referring Expression Comprehension (REC) that focuses on single-target expressions, GREC extends the scope to a more practical setting by further encompassing no-target and multi-target expressions. Existing REC methods face challenges in handling the complex cases encountered in GREC, primarily due to their fixed output and limitations in multi-modal representations. To address these issues, we propose a Hierarchical Alignment-enhanced Adaptive Grounding Network (HieA2G) for GREC, which can flexibly deal with various types of referring expressions. First, a Hierarchical Multi-modal Semantic Alignment (HMSA) module is proposed to incorporate three levels of alignments, including word-object, phrase-object, and text-image alignment. It enables hierarchical cross-modal interactions across multiple levels to achieve comprehensive and robust multi-modal understanding, greatly enhancing grounding ability for complex cases. Then, to address the varying number of target objects in GREC, we introduce an Adaptive Grounding Counter (AGC) to dynamically determine the number of output targets. Additionally, an auxiliary contrastive loss is employed in AGC to enhance object-counting ability by pulling in multi-modal features with the same counting and pushing away those with different counting. Extensive experimental results show that HieA2G achieves new state-of-the-art performance on the challenging GREC task and also the other 4 tasks, including REC, Phrase Grounding, Referring Expression Segmentation (RES), and Generalized Referring Expression Segmentation (GRES), demonstrating the remarkable superiority and generalizability of the proposed HieA2G. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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Model Framework of Emergency Evacuation System Based on Multi-Paradigm Modeling 32

Model Framework of Emergency Evacuation System Based on Mult...

引用

第32届中国控制与决策会议

作者： Guo Haoxiang Hu Yuling Liu Jingwan Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Because complex systems contain multiple hybrid and interactive subsystems,it poses a huge challenge to simulation *** research based on multi-paradigm modeling has become an important method for the study of complex *** evacuation system is a typical complex system,which has complex interaction among evacuation individual,emergency environment and emergency *** order to solve the problem of reusability,interoperability and configurability of evacuation system simulation models and improve the efficiency of model utilization,this paper proposes a multi-paradigm modeling framework for emergency evacuation research.A meta-model method based on three paradigms of agent,system dynamics and discrete events is proposed for constructing evacuation individuals,emergency environments and emergency events *** gives a new idea for constructing evacuation simulation system models.

关键词： Complex system Evacuate Multi-paradigm modeling meta-model

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Parameters Compressing in Deep Learning

引用

Computers, Materials & Continua 2020年第1期62卷 321-336页

作者： Shiming He Zhuozhou Li Yangning Tang Zhuofan Liao Feng Li Se-Jung Lim School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on TransportationChangsha University of Science and TechnologyChangsha410114China Liberal Arts&Convergence Studies Honam UniversityGwangju62399Korea

With the popularity of deep learning tools in image decomposition and natural language processing,how to support and store a large number of parameters required by deep learning algorithms has become an urgent problem to be *** parameters are huge and can be as many as *** present,a feasible direction is to use the sparse representation technique to compress the parameter matrix to achieve the purpose of reducing parameters and reducing the storage *** methods include matrix decomposition and tensor *** let vector take advance of the compressing performance of matrix decomposition and tensor decomposition,we use reshaping and unfolding to let vector be the input and output of Tensor-Factorized Neural *** analyze how reshaping can get the best compress *** to the relationship between the shape of tensor and the number of parameters,we get a lower bound of the number of *** take some data sets to verify the lower bound.

关键词： Deep neural network parameters compressing matrix decomposition tensor decomposition

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Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network 39

Multi-Pair Temporal Sentence Grounding via Multi-Thread Know...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Fang, Xiang Fang, Wanlong Wang, Changshuo Liu, Daizong Tang, Keke Dong, Jianfeng Zhou, Pan Li, Beibei Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China Nanyang Technological University Singapore Peking University China Guangzhou University China Zhejiang Gongshang University China

ISBN: (纸本)157735897X

Given some video-query pairs with untrimmed videos and sentence queries, temporal sentence grounding (TSG) aims to locate query-relevant segments in these videos. Although previous respectable TSG methods have achieved remarkable success, they train each video-query pair separately and ignore the relationship between different pairs. To this end, in this paper, we pose a brand-new setting: Multi-Pair TSG, which aims to co-train these pairs. We propose a novel video-query co-training approach, Multi-Thread Knowledge Transfer Network, to locate a variety of video-query pairs effectively and efficiently. Firstly, we mine the spatial and temporal semantics across different queries to cooperate with each other. To learn intra- and inter-modal representations simultaneously, we design a cross-modal contrast module to explore the semantic consistency by a self-supervised strategy. To fully align visual and textual representations between different pairs, we design a prototype alignment strategy to 1) match object prototypes and phrase prototypes for spatial alignment, and 2) align activity prototypes and sentence prototypes for temporal alignment. Finally, we develop an adaptive negative selection module to adaptively generate a threshold for cross-modal matching. Extensive experiments show the effectiveness and efficiency of our proposed method. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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Micromotion Gesture Recognition Technology Based on Millimeter-wave Radar

Micromotion Gesture Recognition Technology Based on Millimet...

引用

IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Zhaoqi Li Fei Gao Yingchun Li Panpan Tang Dayu Cai Zheyi Li Cong Wang Jingchang Nan Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao China School of Information Science and Engineering Harbin Institute of Technology Weihai China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

Different from the traditional FMCW radar signal processing method, this paper mainly explores the application of the time-frequency analysis method of the reflected intermediate frequency of millimeter wave radar in fretting detection. The radar echo data are analyzed by FFT, wavelet transform and Hilbert yellow transform. This topic mainly uses human gesture targets as the micromotion feature to detect. Under the three designed gestures, the time-frequency analysis method proposed in this topic combined with a convolutional neural network achieves a high recognition success rate. The motion detection technology proposed in this paper can be applied to millimeter wave radar under THE FMCW system. It can detect and classify tiny motions under complex backgrounds and show good recognition ability under strong interference. It can be used in medical treatment, automobile and other fields to play the role of accurate identification.

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Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension

arXiv

引用

arXiv 2025年

In this work, we address the challenging task of Generalized Referring Expression Comprehension (GREC). Compared to the classic Referring Expression Comprehension (REC) that focuses on single-target expressions, GREC extends the scope to a more practical setting by further encompassing notarget and multi-target expressions. Existing REC methods face challenges in handling the complex cases encountered in GREC, primarily due to their fixed output and limitations in multi-modal representations. To address these issues, we propose a Hierarchical Alignment-enhanced Adaptive Grounding Network (HieA2G) for GREC, which can flexibly deal with various types of referring expressions. First, a Hierarchical Multi-modal Semantic Alignment (HMSA) module is proposed to incorporate three levels of alignments, including word-object, phrase-object, and text-image alignment. It enables hierarchical cross-modal interactions across multiple levels to achieve comprehensive and robust multi-modal understanding, greatly enhancing grounding ability for complex cases. Then, to address the varying number of target objects in GREC, we introduce an Adaptive Grounding Counter (AGC) to dynamically determine the number of output targets. Additionally, an auxiliary contrastive loss is employed in AGC to enhance object-counting ability by pulling in multi-modal features with the same counting and pushing away those with different counting. Extensive experimental results show that HieA2G achieves new state-of-the-art performance on the challenging GREC task and also the other 4 tasks, including REC, Phrase Grounding, Referring Expression Segmentation (RES), and Generalized Referring Expression Segmentation (GRES), demonstrating the remarkable superiority and generalizability of the proposed HieA2G. Copyright © 2025, The Authors. All rights reserved.

关键词： Electric grounding

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