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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yichong Li Xiaojian Liu Fan Cheng Xiaoyong Pan Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions.

关键词： Proteins Protein engineering Accuracy RNA Machine learning Predictive models Data models Gene expression Bioinformatics Diseases

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Construction of Three-Dimensional Nickel-Vanadium Hydrotalcite with Ball-Flower Architecture for Screen-Printed Asymmetric Supercapacitor

SSRN

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SSRN 2022年

作者： Tu, Qian Zhang, Qiang Sun, Xinyu Wang, Jinglu Lin, Baoying Liu, Jin Liu, Zhonghua Chen, Liangzhe School of Electronic Information Engineering Jingchu University of Technology Jingmen448000 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan430074 China

As a promising energy storage matter, two-dimensional (2D) layered double hydroxides (LDHs) suffer from a lower specific capacitance and poor retention. Morphology engineering is deemed to be an effective means. Herein, three-dimensional (3D) nickel-vanadium hydrotalcite (NiV-LDHs) with ball-flower structure are synthesized successfully via a facile dynamic-refluxing route, such a green strategy dispenses with template and high pressure and generates a specific surface area as high as 61.6 m 2 /g. Then, the screen-printed inks with outstanding rheological performances are formulated and the resulting electrodes display superior hydrophilic performance. Benefitted from the 3D architecture and shear flow process, the NiV-LDHs electrode can deliver an enhanced specific capacitance of 1069 F/g at 1 A/g with cycling stability of ~68.0% after 1500 cycles at 20 A/g, in contrast with that of NiV-LDHs prepared by coprecipitation (848 F/g and ~5.1%). Furthermore, an asymmetric NiV-LDHs//activated carbon supercapacitor (ASC) is assembled, which can yield a remarkable energy density of 75.8 μWh/cm2 at a power density of 0.80 mW/cm2, and two ASCs in series can illuminate a red light (2.5 V) for more than 270 s. Therefore, this study proposes a facile and economic strategy to prepare 3D nanomaterial for advanced and flexible printable energy storage devices. © 2022, The Authors. All rights reserved.

关键词： Screen printing

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Multiple phase transitions in networks of directed networks

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Physical Review E 2019年第1期99卷 012312-012312页

作者： Xueming Liu Linqiang Pan H. Eugene Stanley Jianxi Gao Key Laboratory of Image Information Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan 430074 Hubei China Department of Physics Center for Polymer Studies Boston University Boston Massachusetts 02215 USA Department of Computer Science Rensselaer Polytechnic Institute Troy New York 12180 USA

The robustness in real-world complex systems with dependency connectivities differs from that in isolated networks. Although most complex network research has focused on interdependent undirected systems, many real-world networks—such as gene regulatory networks and traffic networks—are directed. We thus develop an analytical framework for examining the robustness of networks made up of directed networks of differing topologies. We use it to predict the phase transitions that occur during node failures and to generate the phase diagrams of a number of different systems, including treelike and random regular (RR) networks of directed Erdős-Rényi (ER) networks and scale-free networks. We find that the the phase transition and phase diagram of networks of directed networks differ from those of networks of undirected networks. For example, the RR networks of directed ER networks show a hybrid phase transition that does not occur in networks of undirected ER networks. In addition, system robustness is affected by network topology in networks of directed networks. As coupling strength q increases, treelike networks of directed ER networks change from a second-order phase transition to a first-order phase transition, and RR networks of directed ER networks change from a second-order phase transition to a hybrid phase transition, then to a first-order phase transition, and finally to a region of collapse. We also find that heterogenous network systems are more robust than homogeneous network systems. We note that there are multiple phase transitions and triple points in the phase diagram of RR networks of directed networks and this helps us understand how to increase network robustness when designing interdependent infrastructure systems.

关键词： Attacks on networks Cascades Continuous phase transition Discontinuous phase transition Network phase transitions Percolation phase transition Random attacks Robustness Topological phase transition

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Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix

arXiv

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arXiv 2023年

作者： Wang, Kewei Wu, Yizheng Pan, Zhiyu Li, Xingyi Xian, Ke Wang, Zhe Cao, Zhiguo Lin, Guosheng S-Lab Nanyang Technological University Singapore Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China SenseTime Research Hong Kong

Class-agnostic motion prediction methods aim to comprehend motion within open-world scenarios, holding significance for autonomous driving systems. However, training a high-performance model in a fully-supervised manner always requires substantial amounts of manually annotated data, which can be both expensive and time-consuming to obtain. To address this challenge, our study explores the potential of semi-supervised learning (SSL) for class-agnostic motion prediction. Our SSL framework adopts a consistency-based self-training paradigm, enabling the model to learn from unlabeled data by generating pseudo labels through test-time inference. To improve the quality of pseudo labels, we propose a novel motion selection and re-generation module. This module effectively selects reliable pseudo labels and regenerates unreliable ones. Furthermore, we propose two data augmentation strategies: temporal sampling and BEVMix. These strategies facilitate consistency regularization in SSL. Experiments conducted on nuScenes demonstrate that our SSL method can surpass the self-supervised approach by a large margin by utilizing only a tiny fraction of labeled data. Furthermore, our method exhibits comparable performance to weakly and some fully supervised methods. These results highlight the ability of our method to strike a favorable balance between annotation costs and performance. Code will be available at https://***/kwwcv/SSMP. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Motor imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces

arXiv

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arXiv 2024年

作者： Wu, Huanyu Li, Siyang Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science Technology Research Institute Shenzhen518063 China

Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user's MI without explicit triggers. They are challenging to implement, because the algorithm needs to first distinguish between resting-states and MI trials, and then classify the MI trials into the correct task, all without any triggers. This paper proposes a sliding window prescreening and classification (SWPC) approach for MI-based asynchronous BCIs, which consists of two modules: a prescreening module to screen MI trials out of the resting-state, and a classification module for MI classification. Both modules are trained with supervised learning followed by self-supervised learning, which refines the feature extractors. Within-subject and cross-subject asynchronous MI classifications on four different EEG datasets validated the effectiveness of SWPC, i.e., it always achieved the highest average classification accuracy, and outperformed the best state-of-the-art baseline on each dataset by about 2%. © 2024, CC BY.

关键词： Self-supervised learning

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Lifetime Extension control for Multi-Stack SOFC System with Inconsistent Stacks

Lifetime Extension Control for Multi-Stack SOFC System with ...

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Chinese control Conference (CCC)

作者： Zexin Li Guoqiang Liu Wei Zhao Xi Li Key Laboratory of Image Processing and Intelligent Control of Education Ministry School of Artificial Intelligence and Automation Huazhong University of Science & Technology Wuhan Hubei China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen Guangdong China

The multi-stack fuel cell system (MFCS) is an important basis for the application of solid oxide fuel cell (SOFC) technology to large-scale power generation systems. MFCS can provide higher power, longer lifetime and better reliability. Different from the single-stack fuel cell system (SFCS), there may be some inconsistencies among the stacks in the MFCS. If the inconsistencies are ignored, they will significantly affect the lifetime of the MFCS. This study focuses on the lifetime extension of multi-stack SOFC power generation systems with inconsistent stacks. Since the multi-stack system of SOFC generally cannot control the flow rate of each stack respectively, it brings a difficulty to the control of the MFCS with inconsistencies among the stacks. In order to overcome this difficulty, this paper built a multi-stack SOFC system model, which can characterize the degradation and inconsistencies among SOFC stacks. And then designed a power distribution strategy based on fuzzy control to extend the lifetime of the MFCS. The strategy was verified by simulations. The simulation results show that the strategy can effectively control the inconsistencies among the stacks and significantly extend the lifetime of the MFCS.

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Adversarial refinement network for human motion prediction

arXiv

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arXiv 2020年

作者： Chao, Xianjin Bin, Yanrui Chu, Wenqing Cao, Xuan Ge, Yanhao Wang, Chengjie Li, Jilin Huang, Feiyue Leung, Howard City University of Hong Kong Hong Kong Hong Kong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China

Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend, but motion details such as limb movement may be lost. To predict more accurate future human motion, we propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation. Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation. During training, we deliberately introduce the error distribution by learning through the adversarial mechanism among different subjects. In testing, our cascaded refinement network alleviates the prediction error from the coarse predictor resulting in a finer prediction robustly. This adversarial error augmentation provides rich error cases as input to our refinement network, leading to better generalization performance on the testing dataset. We conduct extensive experiments on three standard benchmark datasets and show that our proposed ARNet outperforms other state-of-the-art methods, especially on challenging aperiodic actions in both short-term and long-term predictions. © 2020, CC-BY.

关键词： Errors

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Route planning based on gradient-field quantum genetic algorithm model

Journal of Software

引用

Journal of Software 2013年第10期8卷 2511-2516页

作者： Sun, Yangguang Ding, Mingyue College of Computer Science South-Central University for Nationalities State Key Laboratory of Software Engineering Wuhan University Wuhan 430074 China School of Life Science and Technology 'Image Processing and Intelligence Control' Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China

A route planning method based on gradient-field quantum genetic algorithm model was presented in this paper. It introduces the gradient field of a grid map to quantum genetic algorithm model and uses quantum genetic algorithm (QGA) to optimize the cost function of route planning. By combining the quantum characteristics with the capabilities of the large diversity of the population, as well as fast convergence rate and high global searching, the optimization of route was guided and realized in our method by using the genetic operators with the essential characteristics of quantum and the gradient information of a grid map. Experimental results demonstrated that our method further effectively improves the quick convergence and capability of searching the optimal route. © 2013 ACADEMY PUBLISHER.

关键词： Cost functions

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Csp-Net: Common Spatial Pattern Empowered Neural Networks for Eeg-Based Motor imagery Classification

SSRN

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SSRN 2024年

作者： Jiang, Xue Meng, Lubin Chen, Xinru Xu, Yifan Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518063 China

Electroencephalogram-based motor imagery (MI) classification is an important paradigm of non-invasive brain-computer interfaces. Common spatial pattern (CSP), which exploits different energy distributions on the scalp while performing different MI tasks, is very popular in MI classification. Convolutional neural networks (CNNs) have also achieved great success, due to their powerful learning capabilities. This paper proposes two CSP-empowered neural networks (CSP-Nets), which integrate knowledge-driven CSP filters with data-driven CNNs to enhance the performance in MI classification. CSP-Net-1 directly adds a CSP layer before a CNN to improve the input discriminability. CSP-Net-2 replaces a convolutional layer in CNN with a CSP layer. The CSP layer parameters in both CSP-Nets are initialized with CSP filters designed from the training data. During training, they can either be kept fixed or optimized using gradient descent. Experiments on four public MI datasets demonstrated that the two CSP-Nets consistently improved over their CNN backbones, in both within-subject and cross-subject classifications. They are particularly useful when the number of training samples is very small. Our work demonstrates the advantage of integrating knowledge-driven traditional machine learning with data-driven deep learning in EEG-based brain-computer interfaces. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Displacement prediction model of landslide based on multi-gene genetic programming

Displacement prediction model of landslide based on multi-ge...

引用

Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Jiejie Chen Zhigang Zeng Ping Jiang College of Computer Science and Technnology Hubei Normal university Huangshi China School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China Computer School Hubei PolyTechnic University Huangshi China

In this paper, a new approach is presented for predicting landslide displacement using multi-gene genetic programming (MGGP). For the characteristic of MGGP which does not need specific assumptions, two real cases is ... 详细信息

ISBN: (纸本)9781509044245

关键词： Terrain factors Mathematical model Predictive models Training Geology Computational modeling Forecasting

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