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An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network

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BMC BIOINFORMATICS 2022年第1期23卷 9-9页

作者： Jiang, Hanjing Huang, Yabing Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Inst Artificial IntelligenceEduc Minist China Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Wuhan Univ Dept Pathol Renmin Hosp Wuhan 430060 Hubei Peoples R China

Background Drug-disease associations (DDAs) can provide important information for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs verified by experiments. Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. How to integrate different biological data sources and identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms is still a challenging problem. Results In this paper, we proposed a novel computation model for DDA predictions based on graph representation learning over multi-biomolecular network (GRLMN). More specifically, we firstly constructed a large-scale molecular association network (MAN) by integrating the associations among drugs, diseases, proteins, miRNAs, and lncRNAs. Then, a graph embedding model was used to learn vector representations for all drugs and diseases in MAN. Finally, the combined features were fed to a random forest (RF) model to predict new DDAs. The proposed model was evaluated on the SCMFDD-S data set using five-fold cross-validation. Experiment results showed that GRLMN model was very accurate with the area under the ROC curve (AUC) of 87.9%, which outperformed all previous works in terms of both accuracy and AUC in benchmark dataset. To further verify the high performance of GRLMN, we carried out two case studies for two common diseases. As a result, in the ranking of drugs that were predicted to be related to certain diseases (such as kidney disease and fever), 15 of the top 20 drugs have been experimentally confirmed. Conclusions The experimental results show that our model has good performance in the prediction of DDA. GRLMN is an effective prioritization tool for screening the reliable DDAs for follow-up studies concerning their participation in drug reposition.

关键词： Drug-disease association Graph representation learning Multi-biomolecular network

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Phenotyping multiple maize ear traits from a single image: Kernels per ear, rows per ear, and kernels per row*

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 193卷 106681-106681页

作者： Shi, Min Zhang, Shuaisong Lu, Hao Zhao, Xiaoming Wang, Xiqing Cao, Zhiguo Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China China Agr Univ Coll Biol Sci Ctr Crop Funct Genom & Mol Breeding Beijing 100193 Peoples R China

Certain traits of maize ears, such as kernels per ear, kernels per row, and rows per ear, are highly related with the grain yield and stress resistance. They can be used as quantitative indices for selecting superior varieties and analyzing experimental results in maize breeding. However, these traits are often manually obtained, which is laborious and prone to error. image-based object counting methods can replace the manual counting procedure safely, which is fast and accurate. Although a constellation of methods have been proposed, problems remain: i) classic image processing-based methods are not robust to variations of illuminations and resolutions;ii) deep learning-based object counting methods can only predict the total kernels per ear. To address this, we propose a high-throughput maize ear phenotyping pipeline. From a single image, our pipeline estimates kernels per ear, rows per ear, and kernels per row in an interpretable way. The image is first input into a Maize Kernel Counting Network (MKNet), which outputs the estimated kernel density map. Then, receiving the density map as input, a Row Mask Generator (RMG) generates a series of masks;each covers a ear row. Naturally, rows per ear and kernels per row can be recovered from the masks. Extensive experiments show that our method can predict kernels per ear, rows per ear, and kernels per row precisely, with a mean absolute error of 7.48, 0.32 and 1.07, respectively. The results show that, compared with existing object counting methods, MKNet can not only reduce the counting error on kernels per ear, but also produce more accurate localization of maize kernels. The visualizations also demonstrate that RMG is robust to the irregular shapes of ear rows.

关键词： Maize kernel counting Convolutional neural network Kernels per row Rows per ear Deep learning

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Federated Multiagent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multimicrogrid Energy Management

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第5期35卷 5902-5914页

作者： Li, Yuanzheng He, Shangyang Li, Yang Shi, Yang Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China China Belt & Rd Joint Lab Measurement & Control Te Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Peoples R China Univ Victoria Dept Mech Engn Victoria BC V8P 5C2 Canada

The utilization of large-scale distributed renewable energy (RE) promotes the development of the multimicrogrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy sufficiency. The multiagent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this article tackles this practical yet challenging issue by proposing a federated MADRL (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm, thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data are not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge National laboratory distributed energy control communication laboratory MG (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL.

关键词： Energy management Data privacy Servers Reinforcement learning Deep learning Renewable energy sources Data security Federated learning (FL) multiagent deep reinforcement learning (MADRL) multimicrogrid (MMG) proximal policy optimization (PPO)

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Temperature Gradient Analyses of a Tubular Solid Oxide Fuel Cell Fueled by Methanol

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Transactions of Tianjin University 2023年第1期29卷 14-30页

作者： Qidong Xu Meiting Guo Lingchao Xia Zheng Li Qijiao He Dongqi Zhao Keqing Zheng Meng Ni Department of Building and Real Estate Research Institute for Sustainable Urban Development(RISUD)&Research Institute for Smart Energy(RISE)The Hong Kong Polytechnic UniversityHong Kong 999077China School of Artifi cial Intelligence and Automation Key Laboratory of Image Processing and Intelligent ControlHuazhong University of Science and TechnologyWuhan 430074China School of Low-Carbon Energy and Power Engineering China University of Mining and TechnologyXuzhou 221116China

Thermal management in solid oxide fuel cells(SOFC)is a critical issue due to non-uniform electrochemical reactions and convective fl ows within the ***,a 2D mathematical model is established herein to investigate the thermal responses of a tubular methanol-fueled *** show that unlike the low-temperature condition of 873 K,where the peak temperature gradient occurs at the cell center,it appears near the fuel inlet at 1073 K because of the rapid temperature rise induced by the elevated current *** the large heat convection capacity,excessive air could not eff ectively eliminate the harmful temperature gradient caused by the large current ***,optimal control of the current density by properly selecting the operating potential could generate a local thermal neutral ***,the maximum axial temperature gradient could be reduced by about 18%at 973 K and 20%at 1073 K when the air with a 5 K higher temperature is ***,despite the higher electrochemical performance observed,the cell with a counter-fl ow arrange-ment featured by a larger hot area and higher maximum temperature gradients is not preferable for a ceramic SOFC system considering thermal ***,this study could provide insightful thermal information for the operating condition selection,structure design,and stability assessment of realistic SOFCs combined with their internal reforming process.

关键词： Solid oxide fuel cell Modeling Methanol fuel Temperature gradient Internal reforming

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Two-stage Train Components Defect Detection Based on Prior Knowledge

Two-stage Train Components Defect Detection Based on Prior K...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Peng, Gang Li, Zhiyong Wan, Shaowei Deng, Zhang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Wuhan Lisai Technol Co Ltd Wuhan 430073 Peoples R China

ISBN: (纸本)9781665491907

The existing method of detecting defects in train components, which relies on visual identification, requires extensive involvement from inspectors and presents certain limitations. In this study, a two-stage defect detection based on prior knowledge was developed, which first detects the types and positions of components, and then conducts targeted detection of possible existing defect types. The algorithm introduces the prior knowledge of the relative spatial position relationship of components and optimizes the detection of sub-components by cascaded convolutional neural networks and local scale-up. In this study, three methods were used, including deep learning, template matching, and quantitative evaluation based on prior knowledge, to perform targeted detection of defect types that may occur in components. Experiments have verified the adaptability and accuracy of the method, demonstrating its high value for engineering applications.

关键词： Template matching

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Guaranteed Cost for an Event-Triggered Consensus Strategy for Interconnected Two Time-Scales Systems With Structured Uncertainty

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第6期52卷 4370-4380页

作者： Lei, Yan Wang, Yan-Wu Morarescu, Irinel-Constantin Xiao, Jiang-Wen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Univ Lorraine CRAN CNRS F-54000 Nancy France

This article proposes the design of an event-triggered control strategy for consensus of interconnected two-time scales systems with structured uncertainty. The control design under consideration ensures also that consensus is achieved with an overall guaranteed cost. Since each system involves processes evolving on both fast and slow time scales, two Zeno-free event-triggered mechanisms are designed to independently decide the sampling and transmission instants for the slow and fast states, respectively. As the first step, we design an event-triggering consensus protocol in the ideal/nominal case when the interconnected systems are not affected by uncertainties and the interactions happen over a fixed interaction network. Next, the results are extended in order to take into account structured uncertainties affecting the systems' dynamics. At this step, we go further and we provide sufficient conditions for event-triggering consensus with a guaranteed overall cost. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

关键词： Interconnected systems Uncertainty Power system dynamics Topology Laplace equations Synchronization Symmetric matrices Event-triggered control guaranteed cost interconnected systems two-time scale

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Thermal constraint control of reversible solid oxide cell for optimal system efficiency

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International Journal of Hydrogen Energy 2025年 134卷 198-211页

作者： Xu, Yuanwu Yin, Jie Wang, Jingjing Wu, Xiao-long Peng, Jingxuan Tang, Ruifeng Zou, Suhang Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Wuhan University of Science and Technology Hubei Wuhan430081 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China School of Information Engineering Nanchang University Jiangxi Nanchang330031 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore China-Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

Reversible solid oxide cell (RSOC) demonstrates considerable promise in multimodal energy conversion architectures, particularly as a technology for hydrogen-based energy storage systems. Nevertheless, under dynamic operating regimes with time-varying current inputs, realizing time-efficient and reliable transitions to steady-state operation remains difficult. This difficulty arises from the intricate coupling of thermal, electrical, and gaseous processes within the RSOC system. This study aims to address these challenges by developing a 5 kW mathematical model of the RSOC system. The proposed model enables systematic investigation of thermal safety issues arising during current switching operations while introducing a thermal constraint control strategy to enhance both system efficiency and thermal safety performance. This efficiency-driven study employs the Grid Search algorithm to identify optimal steady-state operating points for the RSOC system, ensuring strict adherence to safety constraints across varying current inputs. Furthermore, by integrating the pre-identified optimal operating points as feedforward control with Model Predictive control (MPC)-based feedback correction, this hybrid strategy effectively mitigates stack temperature gradient overshoot while synchronously ensuring optimal system efficiency throughout current transition processes. Under simulated variable current inputs, the proposed RSOC system model and control strategy demonstrate the capability to constrain stack temperature gradient overshoot within 2.3K while enhancing transient efficiency by 5 %–8 %, achieving a peak system efficiency of 85 %. It provides a novel methodology for safe and efficient working with intermittent renewable energy. © 2025 Hydrogen Energy Publications LLC

关键词： Thermal variables control

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A Gaze-Guided Human Locomotion Intention Recognition and Volitional control Method for Knee-Ankle Prostheses

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Chen, Xinxing Chen, Zhaokai Wang, Yuxuan Yin, Shucong Chen, Chuheng Huang, Jian Fu, Chenglong Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen518055 China Shanghai Jiaotong University Shanghai China

This paper presents a novel gaze-guided volitional control method for knee-ankle prostheses, designed to enhance the precision and intuitiveness of prosthetic control in complex locomotion tasks. The method utilizes an eye-tracking system coupled with an RGBD camera to estimate 3D gaze points, which are then used to construct a gaze attention map. This map serves as the basis for predicting human locomotion intentions, enabling the prosthesis to accurately step on the intended foot placement. Experimental validation was conducted with both a healthy subject and an amputee subject, focusing on level-ground walking and stair ascent tasks. The results demonstrated that the prosthesis could achieve precise foot placement, with a mean error of 1.22 cm ± 3.14 cm for the healthy subject and 3.40 cm ± 0.66 cm for the amputee subject during level-ground walking. In stair ascent tasks, the system successfully adapted to various gait patterns, with the prosthetic joint angles closely matching natural human movements. The proposed method also effectively reduced the root mean square error (RMSE) of 3D gaze estimation to 2.70 cm, 2.63 cm, and 1.97 cm along the x, y, and z axes, respectively. These findings indicate that the gaze-guided volitional control method not only enhances the functionality of knee-ankle prostheses but also offers a more intuitive interface for users, aligning prosthetic actions with the user’s visual focus and intentions. © 2016 IEEE.

关键词： Knee prostheses

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Event-Triggered Fixed-Time Stabilization of Two Time Scales Linear Systems

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IEEE TRANSACTIONS ON AUTOMATIC control 2023年第3期68卷 1722-1729页

作者： Lei, Yan Wang, Yan-Wu Morarescu, Irinel-Constantin Postoyan, Romain Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Wuhan 430074 Peoples R China Univ Lorraine Ctr Natl Rech Sci CRAN F-54000 Nancy France

This article investigates the fixed-time stabilization of uncertain linear time-invariant systems exhibiting two time scales using a state-feedback event-triggered controller. We proceed by emulation and assume that we know a controller that solves the problem in the absence of sampling. We then take sampling into account and present an event-triggered strategy, which consists of two independent sampling mechanisms, associated with the slow and the fast subsystems, respectively. In this setting, the fixed-time stability property becomes practical, where the adjustable parameters are constants used to define the triggering rules. The existence of a strictly positive time between any two successive transmissions is ensured for each transmission law. A numerical example is provided to illustrate the effectiveness of the results.

关键词： Asymptotic stability Symmetric matrices Numerical stability Stability criteria Standards Convergence Linear systems Event-triggered control fixed-time stabilization two time scales system

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Aggregating Region Context Information for Semantic Segmentation 13

Aggregating Region Context Information for Semantic Segmenta...

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13th International Conference on Graphics and image processing (ICGIP)

作者： Guo, Zilin Wu, Dongyue Zhang, Qiang Sang, Nong Yu, ChangQian Gao, Changxin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Beijing Inst Tracking & Commun Technol Beijing Peoples R China

ISBN: (数字)9781510650435

ISBN: (纸本)9781510650435;9781510650428

Recently, lots of works try to capture contextual information to benefit semantic segmentation problems. However, most approaches adopt the uniform method to obtain context information, which means each pixel gets its context from the same region. We argue that for each pixel, contextual information aggregated from the region it belongs to can benefit the dense prediction, while those from other irrelevant regions possibly mislead the prediction. In this work, we propose a Region Context Module (RCM) that aggregates context for each pixel only from its object region. Furthermore, we design a Region Context Network (RCNet) embedded in the ASPP Module and Region Context Module. We conduct experiments on three datasets: Cityscapes, Vaihingen and Potsdam datasets. Extensive quantitative and qualitative evaluations demonstrate our model achieves favorable performance against state-of-the-art approaches.

关键词： Region context semantic segmentation

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