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Chinese Control and Decision Conference, CCDC

作者： Tong Qin Lifu Du Yihui Ruan Jingqi Li Lei Liu Huijin Fan National Key Laboratory of Multispectral Information Intelligent Processing Technology. School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In this paper, a data-driven algorithm based on singular perturbation (SP) and reinforcement learning (RL) for the elastic launch vehicle attitude control and elastic suppression has been proposed. Considering that the rigid body state and elastic mode exhibit different time-scale properties, the model is initially decomposed into a stiff body slowly changing subsystem and an elastic fast changing subsystem by employing the SP theory. In addition, a data-driven off-policy learning algorithm is proposed to address the problem of the inability to obtain a precise mathematical model due to aerodynamic parameter perturbation during flight. Without using the precise mathematical model of the system, the algebraic Riccati equation (ARE) of the subsystem can be solved iteratively to obtain its control law, then the control law of the original system is obtained through composite control strategy. Finally, the elastic suppression performance of the proposed method and using a notch filter is compared, which demonstrates the proposed method has a better performance and the rigid body attitudes converge more quickly and steadily.

关键词： Attitude control System dynamics Perturbation methods Heuristic algorithms Riccati equations Reinforcement learning Aerodynamics

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Transductive Multi-Prototype Network for HRRP Recognition with Missing Aspects

Transductive Multi-Prototype Network for HRRP Recognition wi...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Mingchen Yuan Jiaqi Liu Xinyang Wang Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China Zhengzhou Academy Of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The High-Resolution Range Profile (HRRP) plays a pivotal role in radar automatic target recognition. However, due to its aspect sensitivity, the absence of aspects within the training data can lead to a decline in recognition accuracy. To mitigate this issue, we introduce a novel Transductive Multi-Prototype Network (TMP-Net), leveraging the intrinsic similarity within test data to propagate labels along a manifold, thereby achieving classification. TMP-Net addresses the aspect sensitivity by utilizing multiple prototypes per class. Additionally, a loss function term is proposed to ensure the existence of the manifold. Experiments on the MSATR dataset demonstrate that TMP-Net outperforms existing methods by 10.22% in recognition accuracy even with only 10% of aspects represented in the training set. Furthermore, analysis reveals the advantages of employing multiple prototypes.

关键词： Manifolds Training Sensitivity Accuracy Target recognition Prototypes Training data Radar Data processing

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An Evolutionary Multi-Objective Neural Architecture Search Approach to Advancing Cognitive Diagnosis in intelligent Education

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Yang, Shangshang Ma, Haiping Bi, Ying Tian, Ye Zhang, Limiao Jin, Yaochu Zhang, Xingyi School of Artificial Intelligence Anhui University Hefei China Institutes of Physical Science and Information Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Electrical and Information Engineering Computational Intelligence Laboratory Zhengzhou University Zhengzhou China School of Computer Science and Technology Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei Anhui China School of Engineering Westlake University Hangzhou China

As a pivotal technique in intelligent education systems, cognitive diagnosis (CD) serves to reveal students’ knowledge proficiency for better tackling subsequent tasks. Unfortunately, due to pursuing high model interpretability, existing manually designed models for CD often hold simplistic architectures, which cannot cope with intricate data in modern education platforms. Furthermore, the bias of human design limits the emergence of novel and effective CD models. To develop interpretable and more effective models, thus this paper proposes an evolutionary multi-objective neural architecture search (NAS) approach for CD. Specifically, we first adopt a comprehensive search space for the NAS task of CD: all candidate models can be encompassed by a general model that deals with three distinct types of inputs. Then, an innovative model interpretability objective is devised to formulate the architecture search task as a bi-objective optimization problem (BOP). To solve the BOP, we employ a multi-objective genetic programming (MOGP) as the search strategy to explore the search space. To make the employed MOGP search well, all architectures are first encoded by trees for easy optimization, and we devise a genetic operation and a population initialization strategy to expedite its convergence. Finally, the proposed approach is actually a MOGP-based NAS approach for CD. Extensive experiments show that CD models searched by the proposed approach exhibit significantly better performance than existing models and hold as good interpretability as handcrafted models. Besides, the effectiveness of the proposed MOGP search strategy, the devised objective, and tailored strategies are validated. IEEE

关键词： Computer architecture

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Research on Optimization of Two-Stage Scheduling for Aluminum Extrusion Machine Manufacturing Line

Research on Optimization of Two-Stage Scheduling for Aluminu...

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Chinese Automation Congress (CAC)

作者： Aoran Zhou Lipeng Qiu Yali Wu Department of Information and Control Engineering Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co Ltd Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In view of the problems of high energy consumption, long inventory time and low production efficiency in the production process of traditional aluminum extrusion machine, this paper establishes an efficient two-stage parallel scheduling optimization model based on the integration of flexible job shop scheduling and assembly sequence planning (IFJSS-ASP). On this basis, an evolutionary algorithm combining strong dominance relationship and simple grouping method (SDREA-SGM) is proposed. The algorithm utilizes a two-stage hybrid chromosome encoding and decoding method to establish a rational mapping from the decision space to the solution space. Furthermore, in order to prevent the model from easily falling into local optima, an adaptive intra-class and inter-class pairing selection strategy has been adopted by the algorithm. This enhances global search and improves local search flexibility. Finally, the feasibility and effectiveness of the algorithm are verified by solving problems of different scales.

关键词： Energy consumption Job shop scheduling Optimization models Aluminum Production Evolutionary computation Encoding Planning Decoding Optimization

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Missing Modality Completion for Multi Frequency Radar HRRP Recognition Using GAN

Missing Modality Completion for Multi Frequency Radar HRRP R...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Qiang Zhou Bingqian Yu Yuanhao Meng Xin Zhang Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High-resolution range profile (HRRP) is one of the commonly used methods in radar automatic target recognition (RATR). Recently, obtaining HRRP under different modalities, such as frequency and polarization, to improve the RATR performance has become an important development direction. However, in practical applications, sensor failures or interference may lead to modality loss during the model testing phase, resulting in decreased performance or even causing the model to fail to function properly. This paper primarily addresses the scenario of frequency modality loss and specifically proposes a modality completion and recognition network with generative adversarial network-embedded (MCRN-GAN) model. The model first generates and completes the missing modality features using the frequency feature transformation loop (FFTL) based on GAN. Subsequently, it fuses multi-modal features based on spatial and channel attention mechanisms to obtain a joint representation of dual-frequency features. Finally, a fully connected layer is used to complete the classification. Experiments on Ku and W frequency modality datasets demonstrates that the proposed method can effectively handle missing modality data after sufficient training without significant performance loss.

关键词： Training Frequency locked loops Target recognition Fuses Radar Interference Feature extraction Generative adversarial networks Data processing Testing

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yajun Ding, Yulian Li, Aimin Fei, Rong Guo, Xie Wu, Fangxiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

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An External Knowledge-Based Sentiment Classification Method for Chinese Short Texts

An External Knowledge-Based Sentiment Classification Method ...

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Neuromorphic computing (ICNC), International Conference on

作者： Zhaoxun Li Huaqing Li Huiwei Wang College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Intelligent Information Processing Chongqing Three Gorges University Chongqing China and Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

With the rapid development of Internet applications, a large number of short texts expressing public opinion with emotional views have been generated. The subtexts of these short texts are complex, which bring a great challenge to emotional classification. In order to solve this problem, this paper proposes a method for sentiment classification of Chinese short texts based on fused external knowledge. Firstly, we construct a prompt text with fused sentiment knowledge, splice the prompt text with the original input text into the pre-training language model, and guide the pre-training model through the prompt. Then, we design label words for the text domain and map the prediction results for classification. The experimental results show that this method exhibits good performance on the Meituan Waimai dataset, with an accuracy of 81.3%, which are 1.8%, 2.2% and 3.5% higher than the mainstream modeling methods such as BERT, TextCNN and ERINE, respectively.

关键词： Vocabulary Analytical models Accuracy Reviews Neuromorphic engineering Knowledge based systems Natural language processing Internet

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Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection

arXiv

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arXiv 2025年

作者： Liang, Hanzhe Zhou, Jie Chen, Xuanxin Dai, Tao Wang, Jinbao Gao, Can College of Computer Science and Software Engineering Shenzhen University China Shenzhen Audencia Financial Technology Institute Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Faculty of Education Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preprocessing as a dual-objective semantic isolator: (1) mitigating cross-semantic interference to the greatest extent feasible and (2) confining anomaly judgements to aligned semantic spaces wherever viable, thereby establishing intra-semantic comparability. Any preprocessing approach achieves this goal through a two-stage process of Semantic-Division and Spatial-Constraints stage. Through systematic deconstruction, we theoretically and experimentally subsume existing preprocessing methods under this theorem via tripartite evidence: qualitative analyses, quantitative studies, and mathematical proofs. Guided by the Fence Theorem, we implement Patch3D, consisting of Patch-Cutting and Patch-Matching modules, to segment semantic spaces and consolidate similar ones while independently modeling normal features within each space. Experiments on Anomaly-ShapeNet and Real3D-AD with different settings demonstrate that progressively finer-grained semantic alignment in preprocessing directly enhances point-level AD accuracy, providing inverse validation of the theorem’s causal logic. Copyright © 2025, The Authors. All rights reserved.

关键词： Fences

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From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos

arXiv

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arXiv 2023年

作者： Chen, Yin Li, Jia Shan, Shiguang Wang, Meng Hong, Richang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Dynamic facial expression recognition (DFER) in the wild is still hindered by data limitations, e.g., insufficient quantity and diversity of pose, occlusion and illumination, as well as the inherent ambiguity of facial expressions. In contrast, static facial expression recognition (SFER) currently shows much higher performance and can benefit from more abundant high-quality training data. Moreover, the appearance features and dynamic dependencies of DFER remain largely unexplored. Recognizing the potential in leveraging SFER knowledge for DFER, we introduce a novel Static-to-Dynamic model (S2D) that leverages existing SFER knowledge and dynamic information implicitly encoded in extracted facial landmark-aware features, thereby significantly improving DFER performance. Firstly, we build and train an image model for SFER, which incorporates a standard Vision Transformer (ViT) and Multi-View Complementary Prompters (MCPs) only. Then, we obtain our video model (i.e., S2D), for DFER, by inserting Temporal-Modeling Adapters (TMAs) into the image model. MCPs enhance facial expression features with landmark-aware features inferred by an off-the-shelf facial landmark detector. And the TMAs capture and model the relationships of dynamic changes in facial expressions, effectively extending the pre-trained image model for videos. Notably, MCPs and TMAs only increase a fraction of trainable parameters (less than +10%) to the original image model. Moreover, we present a novel Emotion-Anchors (i.e., reference samples for each emotion category) based Self-Distillation Loss to reduce the detrimental influence of ambiguous emotion labels, further enhancing our S2D. Experiments conducted on popular SFER and DFER datasets show that we have achieved a new state of the art. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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SERGCN: Graph Convolutional Neural Networks with Skip-Edge Residual Connection for Node Classification Tasks

SERGCN: Graph Convolutional Neural Networks with Skip-Edge R...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Aqian Qin Guoqing He Huiwei Wang College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Intelligent Information Processing Chongqing Three Gorges University Chongqing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Graph Convolutional Networks (GCNs) have been widely used for node categorization, graph embedding, and link prediction, etc. Two main challenges regarding developing deep GCNs for node classifying are overfitting and oversmoothing. The main causes of oversmoothing are overfitting and training barriers, which is directly caused by gradient disappearance. To resolve this issue, we propose the Skip-Edge Residual Graph Con-volution Network (SERGCN), which extracts a certain proportion of edge information at each layer in the novel way and integrates the initial information and the information of the previous layer in the convolution process, so as to prevent and slow down the gradient disappearance and further oversmoothing. Based on extensive experiments and real-world data sets used in several benchmarks, our proposed solution incorporates the benefits of a deep architecture while equaling or even exceeding the accuracy of existing approaches.

关键词： Training Degradation Accuracy Graph convolutional networks Convolution Deep architecture Benchmark testing Data mining Informatics

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