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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China 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)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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Spatially Dense Multi-Target Track Segment Association Algorithm

Spatially Dense Multi-Target Track Segment Association Algor...

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第42届中国控制会议

作者： Yan Yao Liping Yan Yuanqing Xia Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

In the field of radar data processing, track interruption seriously affects target tracking, track fusion, and other *** existing track segment association algorithms have low correlation accuracy in dense distributed or long-time interruption situations. To this purpose, a dense multi-target track segment association(DMTTSA) algorithm is proposed. Firstly, two identical networks based on the multi-head probability sparse(ProbSparse) self-attention are used to capture the long-term dependencies of the tracks. Then, the bidirectional quadruplet hard sample loss(BiQuaHard loss) is constructed to make the tracks belonging to the same targets closer and the tracks belonging to the different targets farther. Finally, DMTTSA takes the closest track pairs in the feature space as the associated tracks and divides the unassociated tracks into the birth and dead tracks in chronological order. Some comparative experiments are carried out to show the anti-noise performance of the DMTTSA, as well as the effectiveness of solving the problem of dense multi-target track interruption.

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Sample-Efficient Reinforcement Learning Based on Dynamics Models via Meta-policy Optimization 6th

Sample-Efficient Reinforcement Learning Based on Dynamics Mo...

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6th International Conference on Cognitive systems and Signal Processing, ICCSIP 2021

作者： Zuo, Guoyu Tian, Zhipeng Huang, Shuai Gong, Daoxiong Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China

ISBN: (纸本)9789811692468

Model-based reinforcement learning (RL) can acquire remarkable sample efficiency, which makes it a suitable choice for applications where experiment data is hard to collect. However, it is difficult to learn an accurate dynamics model fully matched with the real-world, and the accuracy of the model usually affects the agent’s final performance. In this paper, we propose a novel model-based RL approach called Meta-policy Optimization method with branched rollouts (MPOBR), which gets rid of strong dependency on an accurate model. In MPOBR, meta-learning is used to train a policy prior on an ensemble of learned dynamics models, so that this prior can be rapidly adapted to the environment when combined with environment rollouts. To reduce the affect of model compounding bias, short model-generated rollouts branched from real data are used to update the meta-policy. The experiments on simulated robotic tasks are designed to verify the effectiveness of our method. Results show that our approach can achieve the same asymptotic performance of state-of-the-art model-free algorithms while significantly reducing sample complexity. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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A Multi-Time-Scale Prediction Method for Blast Furnace Gas Utilization Rate Based on Causality Analysis

A Multi-Time-Scale Prediction Method for Blast Furnace Gas U...

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2023 China Automation Congress, CAC 2023

作者： Zhao, Guoyu An, Jianqi Guo, Yunpeng Yang, Chenglin He, Yong School of Future Technology China University of Geosciences China School of Automation China University of Geosciences China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798350303759

Blast furnace operating parameters regulate the gas utilization rate (GUR), and different operating parameters affect the GUR on different time scales. However, the existing methods only analyze and model the prediction on a single-time scale, and the accuracy of the model needs to be improved. In this paper, a multi-time-scale prediction method of blast furnace gas utilization rate based on causality is proposed. Firstly, according to the production characteristics of the blast furnace, burden and blast supply are the operation means to regulate the blast furnace on different time scales. Then, this paper presents a blast furnace operation parameter extraction method fusing correlation coefficient and information entropy, and selects two most informative parameters to characterize the operation. On this basis, the causal analysis method is used to prove that the selected operation parameters affect the development trend of GUR. Finally, the long and short time scale prediction models of GUR are established, which are verified by the data of the actual blast furnace production process, improving the prediction accuracy of the GUR efficiently. © 2023 IEEE.

关键词： Blast furnaces

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Image-Based Visual Servoing under Field of View Constraints for Robot Manipulators 12

Image-Based Visual Servoing under Field of View Constraints ...

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12th International Conference on CYBER Technology in Automation, Control, and intelligent systems, CYBER 2022

作者： Zuo, Guoyu Liu, Yan Huang, Gao Gong, Daoxiong Faculty of Information Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The challenge in image-based visual servoing is to deal with the visibility constraints, which require the image features to always remain in the field of view (FOV) of the camera. In this paper, a novel constraint function is proposed to address this problem. The FOV constraint function represented by the coordinates of the features and the region boundaries defined in the image is added to the classical proportional controller, which leads the image features from the margin to the center of the image. Simulation results verify the efficiency of the modified controller by controlling a robot manipulator with eye-in-hand configuration. © 2022 IEEE.

关键词： Robot applications

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Adaptive Sliding Mode Control for Flexible Dual-manipulator with Friction Compensation

Adaptive Sliding Mode Control for Flexible Dual-manipulator ...

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2022 Chinese Automation Congress, CAC 2022

作者： Song, Tao Zheng, Shiqi Sun, Jiachun Li, Liangguang Chang, Qing Li, Zhipeng China University of Geosciences Wuhan Campus School of Automation China Hubei Key Laboratory of Advanced Control and Intelligent Automation Fo Complex Systems Wuhan China

ISBN: (纸本)9781665465335

This paper focuses on the coordinated tracking control scheme of dual-manipulator based on friction compensation. First, a new dual-manipulator model with flexible joints and friction is constructed;Second, a new adaptive neural control method is proposed for the dual-manipulators with flexible joints. Neural networks are used to approximate the unknown nonlinear dynamics in the model;Third, new state observers are constructed to estimate the joint friction. Based on the observers, the friction can be well compensated. The stability of the system is derived by using Lyapunov method. The simulation results verify the effectiveness of the practical method. © 2022 IEEE.

关键词： Lyapunov methods

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A survey on deep learning for polyp segmentation: techniques, challenges and future trends

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Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

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Multimodal Multilevel Attention for Semi-Supervised Skeleton-Based Gesture Recognition

SSRN

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SSRN 2024年

作者： Liu, Jinting Gan, Ming-Gang He, Yuxuan Guo, Jia Hu, Kang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

Although skeleton-based gesture recognition based on supervised learning has made promising achievements, the reliance on large amounts of annotation for training poses a significant cost. This paper addresses semi-supervised gesture recognition, aiming to learn feature representations effectively from unlabeled data. To resolve this problem, we propose a novel multimodal multilevel attention network to achieve semi-supervised learning. This model uses an attention mechanism to polymerize multimodal and multilevel complementary semantic information of the hand skeleton, designing a multimodal multilevel contrastive loss to measure the similarity of features for semi-supervised learning. Specifically, our method explores the relationships between joint, bone, and motion to learn more discriminative feature representations. Considering the hierarchy of the hand skeleton, the skeleton data is divided into multilevel (i.e., hand-level, finger-level, and joint-level) to capture complementary semantic information. Furthermore, a multimodal contrastive loss measure similarity among multilevel representations. The proposed method outperforms the semi-supervised skeleton-based gesture recognition tasks, according to extensive experimentation on two challenging datasets from SHREC-17 and DHG 14/28 datasets. © 2024, The Authors. All rights reserved.

关键词： Gesture recognition

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Research on Image Segmentation of Power Line Based on Encoder-Decoder Network 16th

Research on Image Segmentation of Power Line Based on Encode...

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16th Annual Conference of China Electrotechnical Society, CES 2021

作者： Sun, Haotian Fang, Zhijian Wei, Zhiguo Xie, Fei School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

ISBN: (纸本)9789811915277

Power line network is the main carrier of the power system, and the smooth patrol inspection plays a crucial role in the fault prevention of the power line network. With the emergence of the patrol inspection UAV, how to detect the target of the power line through the picture and carry out accurate positioning has become a major research focus of the intelligent power system. A method for detecting and locating power lines based on an encoding lightweight decoder network (EDN-light) is proposed in this paper. It provides a foundation for the research and development of intelligent inspection of power line network, such as detection and identification, fault location, and guarantee of operation safety, and puts forward a new solution to the fault location diagnosis of power line using image. By EDN-light network, it focuses on detecting high level target and eliminating irrelevant information such as background, the convolutional layer of the encoder is used for extracting features, and the deconvolutional layer of the decoder for reconstructing images. The EDN-light network can improve the detection accuracy, and generate high-quality segmentation object suggestions. Use Turkish Electricity Transmission Corporation's aviation power line network dataset and self-made dataset to train the model to improve the generalization ability of the model. Experiments have found that due to the fully convolutional network design, it can carry out multi-scale input, For power line image segmentation and detection, the accuracy is better, and the evaluation indicators of the proposed models have increased. And due to the design of the lightweight decoder, Without retraining the network, the learned model can be extended to unknown linear category targets without repeated training. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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Geometry Problem Solving Based on Counter-Factual Evolutionary Reasoning 19

Geometry Problem Solving Based on Counter-Factual Evolutiona...

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19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Song, Bing Xiong, Gang Shen, Zhen Zhu, Fenghua Lv, Yisheng Ye, Peijun Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences The Beijing Engineering Research Center of Intelligent Systems and Technology China Chinese Academy of Sciences The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Dongguan523808 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266000 China

ISBN: (纸本)9798350320695

As a representative topic in natural language processing and automated theorem proving, geometry problem solving requires an abstract problem understanding and symbolic reasoning. A major challenge here is to find a feasible reasoning sequence that is consistent with given axioms and the theorems already proved. Most recent methods have exploited neural network-based techniques to automatically discover eligible solving steps. Such a kind of methods, however, is greatly impacted by the expert solutions for training. To improve the accuracy, this paper proposes a new method called counterfactual evolutionary reasoning, which uses a generative adversarial network to generate initial reasoning sequences and then introduces counterfactual reasoning to explore potential solutions. By directly exploring theorem candidates rather than the neural network selection, the new method can sufficiently extend the searching space to get a more appropriate reasoning step. Through comparative experiments on the recent proposed Geometry3k, the largest geometry problem solving dataset, our method generally achieves a higher accuracy than most previous methods, bringing an overall improvement about 4.4% compared with the transformer models. © 2023 IEEE.

关键词： Geometry

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