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arXiv 2025年

作者： Li, Jiang Wang, Xiaoping School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Brain-Inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Protein-protein interaction (PPI) prediction is an instrumental means in elucidating the mechanisms underlying cellular operations, holding significant practical implications for the realms of pharmaceutical development and clinical treatment. Presently, the majority of research methods primarily concentrate on the analysis of amino acid sequences, while investigations predicated on protein structures remain in the nascent stages of exploration. Despite the emergence of several structure-based algorithms in recent years, these are still confronted with inherent challenges: (1) the extraction of intrinsic structural information of proteins typically necessitates the expenditure of substantial computational resources;(2) these models are overly reliant on seen protein data, struggling to effectively unearth interaction cues between unknown proteins. To further propel advancements in this domain, this paper introduces a novel PPI prediction method jointing masked reconstruction and contrastive learning, termed JmcPPI. This methodology dissects the PPI prediction task into two distinct phases: during the residue structure encoding phase, JmcPPI devises two feature reconstruction tasks and employs graph attention mechanism to capture structural information between residues;during the protein interaction inference phase, JmcPPI perturbs the original PPI graph and employs a multi-graph contrastive learning strategy to thoroughly mine extrinsic interaction information of novel proteins. Extensive experiments conducted on three widely utilized PPI datasets demonstrate that JmcPPI surpasses existing optimal baseline models across various data partition schemes. The associated code can be accessed via https://***/lijfrankopen/JmcPPI. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges

arXiv

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arXiv 2022年

作者： Li, Siyang Xu, Yifan Wu, Huanyu Wu, Dongrui Yin, Yingjie Cao, Jiajiong Ding, Jingting Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Ant Group Hangzhou China

Facial affect analysis remains a challenging task with its setting transitioned from lab-controlled to in-the-wild situations. In this paper, we present novel frameworks to handle the two challenges in the 4th Affective Behavior Analysis In-The-Wild (ABAW) competition: i) Multi-Task-Learning (MTL) Challenge and ii) Learning from Synthetic Data (LSD) Challenge. For MTL challenge, we adopt the SMM-EmotionNet with a better ensemble strategy of feature vectors. For LSD challenge, we propose respective methods to combat the problems of single labels, imbalanced distribution, fine-tuning limitations, and choice of model architectures. Experimental results on the official validation sets from the competition demonstrated that our proposed approaches outperformed baselines by a large margin. The code is available at https://***/sylyoung/ABAW4-HUST-ANT. © 2022, CC BY-NC-ND.

关键词： Face recognition

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A Spatio-Temporal Scale Based Affective Neural Network for Emotion Detection in Conversation

A Spatio-Temporal Scale Based Affective Neural Network for E...

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Chinese Automation Congress (CAC)

作者： Jiang Li Xiaoping Wang Shanglin Lei Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology (HUST) Wuhan China Institute of Artificial Intelligence Huazhong University of Science and Technology (HUST) Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Key Laboratory of Brain-inspired Intelligent Systems Hubei Province Wuhan China

Emotion Detection in Conversation (EDC) has been implemented in numerous domains and is under increasing attention. The EDC task is highly challenging due to the need to incorporate both the context of the utterance and the identity of the participant. Some existing EDC approaches employ redundant network structures and inadequately model contextual utterances, resulting in limited performance gains. In this work, we devise a Spatio-Temporal scale based Affective neural Network (STANet) in a simplistic and efficacious manner. STANet consists of two main components, i.e., spatial scale network and temporal scale network, which are applied to extract spatial scale and temporal scale information in the conversation, respectively. Prior to spatio-temporal scale modeling, we encode the identity information of participants to examine its impact on the performance of emotion detection. We conduct substantial comparative experiments and ablation studies on three conversational emotion datasets, and the results demonstrate that our STANet realizes superior performance without taking advantage of external knowledge.

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Multi-storage, multi-energy, and multi-policy optimization for hybrid green and grey hydrogen production: A regional study in China

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Energy Conversion and Management 2025年 334卷

作者： Yin, Zishan Tang, Xiyun Li, Xi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China Key Laboratory of Imaging Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China

Green hydrogen is key to global decarbonization. This study optimizes regional green-grey hydrogen production in China, using a multi-storage (hydrogen and battery) and multi-renewable energy model. The focus is on minimizing the levelized cost of hydrogen and carbon emission factor, incorporating carbon reduction policies. The results show that the tradable green certificates policy leads to a lower cost, while the cap-and-trade mechanism accelerates the achievement of grey hydrogen substitution across China's regions by 20 years to 2060. Regions rich in renewable energy achieve $5/kg lower costs and 6 years faster substitution. Hydrogen storage is the most cost-effective solution, hybrid storage is preferable in low renewable energy areas, while battery storage incurs costs that are 25% to 150% higher. The trade-off between carbon emission and cost highlights each region's inflection point, essential for designing incentive policies. © 2025 Elsevier Ltd

关键词： Battery storage

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Simulation-based Method for Determing Parameters of Material Price Adjustment Model in Large-scale Hydropower Development in China

Simulation-based Method for Determing Parameters of Material...

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2021智慧城市与智能建造高端论坛

作者： Lijing Yang Zhenyuan Liu Xi Chen Yaodi Fan Hualin Chen School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Education Ministry for Image Processing and Intelligent Control Yalong River Hydropower Development Company LTD

In the construction of large-scale hydropower projects in China,enormous amounts of materials are *** by various factors,fluctuation of materials price is very common during the *** protect the benefits of the demander and the supplier,reasonable material price adjustment methods need to be set in the procurement bidding documents.A price adjustment model is established considering price fluctuation stratifying and risk sharing.A simulation-based method is proposed for determing parameters in the model,and the simulation process is optimized with the optimal computing budget allocation *** on the judgment of engineers and real data of a hydropower project in Sichuan,China,and simulation cases are generated and the experimental results are analyzed with the *** results show that the simulation-based method provides scientific and reasonable support for determing parameters of material price adjustment model,and the optimal computing budget allocation algorithm plays an optimizing role.

关键词： Large-scale Hydropower Development Material Price Adjustment Parameters Determing Simulation Optimal Computing Budget Allocation TOPSIS

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Computationally Expensive High-Dimensional Multiobjective Optimization via Surrogate-Assisted Reformulation and Decomposition

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Pan, Linqiang Lin, Jianqing Wang, Handing He, Cheng Tan, Kay Chen Jin, Yaochu School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence Xidian University Xi’an China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong School of Engineering Trustworth and General Artificial Intelligence Laboratory Westlake University Hangzhou China

In recent decades, various surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve computationally expensive multiobjective optimization problems (EMOPs). Nevertheless, designing an SAEA to handle high-dimensional EMOPs and balance convergence, diversity, and computational complexity remains challenging. Here, we propose a two-phase SAEA (TP-SAEA), which follows the idea of convergence first and diversity second, for solving high-dimensional EMOPs. In Phase I, a surrogate-assisted problem reformulation method is proposed to fast-track the Pareto optimal set in association with some reference solutions. Specifically, the high-dimensional EMOP is reformulated into an expensive single-objective one with low-dimensional decision space. Then, the surrogate-assisted optimization is utilized to obtain well-converged solutions. In Phase II, the high-dimensional EMOP is decomposed into two subproblems to explore subregions of the decision space that can effectively promote the diversity of the solutions. The two subproblems are optimized independently via surrogate-assisted optimization, aiming to push the population towards different regions of the Pareto optimal front. Experiments are conducted on EMOPs with 100 to 500 decision variables compared with four state-of-the-art SAEAs. The proposed TP-SAEA obtains well-converged and diverse solutions with only 509 real function evaluations. Moreover, its superiority is examined in six real-world instances with up to 12,000 decision variables. IEEE

关键词： Iron

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Systematic Cloud-Based Optimization: Twin-Fold Moth Flame Algorithm for VM Deployment and Load-Balancing

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Intelligent Automation & Soft Computing 2024年第3期39卷 477-510页

作者： Umer Nauman Yuhong Zhang Zhihui Li Tong Zhen Information Science and Engineering College Henan University of TechnologyZhengzhou450001China School of Artificial Intelligence and Big Data Henan University of TechnologyZhengzhou450001China Key Laboratory of Grain Information Processing and Control Ministry of EducationHenan University of TechnologyZhengzhou450001China

Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and ***,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data *** the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing *** design employs load-balancing techniques to allocate different user workloads across several virtual *** address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization *** intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource *** provides a thorough approach to evaluating total costs in the cloud computing *** assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource *** investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load *** proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.

关键词： Optimizing cloud computing deployment of virtual machines load-balancing twin-fold moth flame algorithm grid computing computational resource distribution data virtualization

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Pcda: Efficient Decision-Based Black-Box Attack for Point Clouds

SSRN

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SSRN 2023年

作者： Liu, Lianguang Lv, Junting Li, Depeng Xu, Bingrong Chen, Chongyang Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China School of Automatio Wuhan University of Technology Wuhan430074 China

Deep neural networks for 3D point clouds have shown significant progress in safety-critical applications. However, the robustness of these deep 3D models is insufficient-explored when faced with adversarial attacks. The existing point clouds attack methods usually rely on detailed model information (white-box attacks) or access to the training data (transfer-based black-box attacks). In this work, we make the first attempt in decision-based black-box attack for point clouds (PCDA), which only leverages the predicted labels of the victim models. Specifically, a novel perturbation projection based on chamfer distance is developed to be compatible with 3D irregular data, and the gradient estimation based on low-dimensional subspaces can generate smoother perturbations. Moreover, armed with iterative closest point, the novel perturbation initialization can improve the effectiveness of targeted black-box attacks. Evaluated on a variety of typical 3D models, our algorithm can efficiently generate smooth 3D adversarial samples with a lower magnitude of perturbations. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Multi-Objective Optimal Configuration and Stable-Dynamic Management Rules of Grid-Connected Sofc/Lb Microgrids Accelerated by Energy-Based Pre-Selection

SSRN

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SSRN 2024年

作者： Yin, Zishan Tang, Xiyun Wang, Zhuo Deng, Zhonghua Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Imaging Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China Wuhan Huamao Automation Co. Ltd. China

Microgrids are suitable to consume the increasing renewable sources to meet the fluctuating load demand nowadays, and configuration design is essential to build techno-economic microgrids. Solid Oxide Fuel Cell (SOFC) has high efficiency, high power output, and high fuel adaptability, which can be implemented as a controllable unit combined with an electrolyzer in hydrogen hybrid microgrids. However, the SOFC system’s slow load response should be considered due to SOFC’s complex dynamic characteristics. In this research, a grid-connected SOFC/lithium battery (LB) microgrid integrated photovoltaic and wind energy is constructed, and the LB complements the SOFC system’s slow load response. The stable-dynamic management rules are proposed, considering both SOFC’s tracking response and LB ‘s dynamic discharging and charging limitation to enhance LB’s cycle times. The minimal life cycle cost and minimal renewable energy (RS) curtailment rate are optimized by NSGA-II and the solving process is accelerated by an energy-based pre-selection method. An actual case is studied in one small region in China. The acceleration method is evaluated by comparison and the trade-off between two objectives is proved by the Pareto of the optimal solutions. To further analyze daytime management, we solve the optimal compromise solution by the fuzzy function and study the power dispatching of the typical days. © 2024, The Authors. All rights reserved.

关键词： Hydrogen

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Spiking Neural Network for Intra-cortical Brain Signal Decoding

arXiv

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arXiv 2025年

作者： Yang, Song Fu, Haotian Zhang, Herui Zhang, Peng Li, Wei 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 Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China Department of Biomedical Engineering College of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Decoding brain signals accurately and efficiently is crucial for intra-cortical brain-computer interfaces. Traditional decoding approaches based on neural activity vector features suffer from low accuracy, whereas deep learning based approaches have high computational cost. To improve both the decoding accuracy and efficiency, this paper proposes a spiking neural network (SNN) for effective and energy-efficient intra-cortical brain signal decoding. We also propose a feature fusion approach, which integrates the manually extracted neural activity vector features with those extracted by a deep neural network, to further improve the decoding accuracy. Experiments in decoding motor-related intra-cortical brain signals of two rhesus macaques demonstrated that our SNN model achieved higher accuracy than traditional artificial neural networks;more importantly, it was tens or hundreds of times more efficient. The SNN model is very suitable for high precision and low power applications like intracortical brain-computer interfaces. © 2025, CC BY.

关键词： Deep neural networks

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