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arXiv 2025年

作者： Wang, Xiang Zhang, Shiwei Tang, Longxiang Zhang, Yingya Gao, Changxin Wang, Yuehuan Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology United States Alibaba Group China Tsinghua University China

This report presents UniAnimate-DiT, an advanced project that leverages the cutting-edge and powerful capabilities of the open-source Wan2.1 model for consistent human image animation. Specifically, to preserve the robust generative capabilities of the original Wan2.1 model, we implement Low-Rank Adaptation (LoRA) technique to fine-tune a minimal set of parameters, significantly reducing training memory overhead. A lightweight pose encoder consisting of multiple stacked 3D convolutional layers is designed to encode motion information of driving poses. Furthermore, we adopt a simple concatenation operation to integrate the reference appearance into the model and incorporate the pose information of the reference image for enhanced pose alignment. Experimental results show that our approach achieves visually appearing and temporally consistent high-fidelity animations. Trained on 480p (832x480) videos, UniAnimate-DiT demonstrates strong generalization capabilities to seamlessly upscale to 720P (1280x720) during inference. The training and inference code is publicly available at https://github. com/ali-vilab/UniAnimate-DiT. Copyright © 2025, The Authors. All rights reserved.

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Multi-Period Multi-Product Supply Chain Network Design in the Competitive Environment

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MATHEMATICAL PROBLEMS IN ENGINEERING 2020年第1期2020卷 1-15页

作者： Wang, Jian Wang, Xueyan Yu, Mingzhu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Shenzhen Univ Inst Big Data Intelligent Management & Decis Coll Management Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China

This paper studies a supply chain network design model with price competition. The supply chain provides multiple products for a market area in multiple periods. The model considers the location of manufacturers and retailers and assumes a probabilistic customer behavior based on an attraction function depending on both the location and the quality of the retailers. We aim to design the supply chain under the capacity constraint and maximize the supply chain profit in the competitive environment. The problem is formulated as a mixed integer nonlinear programming model. To solve the problem, we propose two heuristic algorithms-Simulated Annealing Search (SA) and Particle Swarm Optimization (PSO)-and numerically demonstrate the effectiveness of the proposed algorithms. Through the sensitivity analysis, we give some management insights.

关键词： Supply chains

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Some necessary and sufficient conditions for containment of second-order multi-agent systems with sampled position data

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NEUROCOMPUTING 2020年第0期378卷 228-237页

作者： Liu, Yifan Su, Housheng Hunan Univ Technol Sch Sci Zhuzhou 412008 Peoples R China Huazhong Univ Sci & Technol Sch Automat Image Proc & Intelligent Control Key Lab Educ Minist China Luoyu Rd 1037 Wuhan 430074 Peoples R China

The containment control problem using only sampled position data for second-order multi-agent systems (MAS) is studied in this paper. We first consider the delay-free case of the containment control protocol and obtain a necessary and sufficient condition regarding the network structure and the system parameters. Then, we obtain a similar necessary and sufficient condition for the case with time delay of the containment control protocol. Finally, several numerical simulations are performed to verify the correctness of the theoretical results. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-agent system Containment control Sampled position data Communication width Sampling period

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Prognostic Prediction for Non-small-Cell Lung Cancer Based on Deep Neural Network and Multimodal Data 17th

Prognostic Prediction for Non-small-Cell Lung Cancer Based o...

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17th International Conference on intelligent Computing, ICIC 2021

作者： Zhang, Zhong-Si Xu, Fei Jiang, Han-Jing Chen, Zhan-Heng Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University No. 4800 Caoan Road Shanghai201804 China

ISBN: (纸本)9783030845315

Non-small-cell lung cancer (NSCLC) is the most common lung cancer with poor prognosis. Prognostic prediction is significant in improving the prognosis of NSCLC patients. Clinical information and multi-omics data including gene expression, miRNA, copy number variations, and DNA methylation are closely related to NSCLC prognosis. In this study, we propose a deep neural network to conduct prognostic prediction for NSCLC patients based on all five types of data. Given the high dimensional features of the omics data, past works reduce the feature dimension by regression analysis or correlation sorting algorithms. A shortcoming of these methods is that only a small number of features are considered. To overcome it, we propose a convolution neural network-based feature transformation method, which considers all features and extracts the abstract representations of the omics data. Based on the representations, we predict the five-year survival status of NSCLC patients. The results show that our method achieves a more precise prediction than previous work, with the areas under the curve improved from 0.8163 to 0.8508 and the accuracy improved from 0.7544 to 0.8096. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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A discretization approach to sampled-data stabilization of networked systems with successive packet losses

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2021年第10期31卷 4589-4601页

作者： Hu, Zhipei Zhang, Jin Deng, Feiqi Fan, Zhun Qiu, Li Shantou Univ Dept Elect & Informat Engn Shantou Peoples R China Shantou Univ Guangdong Prov Key Lab Digital Signal & Image Pro Shantou Peoples R China Shantou Univ Minist Educ Key Lab Intelligent Mfg Technol Shantou Peoples R China Tel Aviv Univ Sch Elect Engn Tel Aviv Israel South China Univ Technol Coll Automat Sci & Engn Guangzhou 510640 Peoples R China Shenzhen Univ Coll Mechatron & Control Engn Shenzhen Peoples R China

This article is concerned with the stabilization problem for a class of networked systems subject to successive packet losses. Different from the input delay approach used in some existing literature, the continuous-time system under consideration is first converted into a discrete-time stochastic system with system matrices subject to stochastic characteristic. In order to deal with the difficulties in the calculations of mathematical expectations of both matrix exponential and integral of matrix exponential function, the upper bound of successive packet losses and packet drop rate are assumed to be known and then the probabilities of the number of successive packet losses taking each value in a bounded set are calculated. Based on this, stability criteria are derived by recurring to the law of total expectation, which guarantee the exponential mean-square stability of resulting closed-loop discrete-time stochastic system with a prescribed H-infinity performance. Moreover, a controller design procedure is then proposed. Finally, to verify the analysis results and testify the effectiveness and applicability of the designed algorithm, a numerical simulation example is given.

关键词： discretization approach networked sampled‐ data systems stochastic systems successive packet losses

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Autonomous Landing Allocation of Multiple Unmanned Aerial Vehicles on Multiple Unmanned Surface Vessels Subject to Energy Consumption 1

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14th International Conference on intelligent Robotics and Applications (ICIRA)

作者： Ye, Jingtian Hu, Bin-Bin Xu, Zhecheng Liu, Bin Zhang, Hai-Tao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China

ISBN: (数字)9783030891343

ISBN: (纸本)9783030891343;9783030891336

This paper proposes a distributed energy-based landing allocation method of multiple unmanned aerial vehicles (UAVs) on multiple unmanned surface vessels (USVs). First, an optimization function is established, which consists of the limited energy battery, the flight distance and the flight speed. Then, a distributed landing allocation scheme is designed to minimize the energy consumption difference. Finally, real lake experiments of multiple M-100UAVs and HUSTER-30 USVs are conducted to verify the effectiveness of the proposed method.

关键词： Autonomous landing allocation Unmanned aerial vehicles (UAVs) Unmanned surface vessels (USVs) Energy consumption

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Prediction of Drug-Disease Associations Based on Long Short-Term Memory Network and Gaussian Interaction Profile Kernel 15th

Prediction of Drug-Disease Associations Based on Long Short-...

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15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Jiang, Han-Jing Wang, Yan-Bin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Computer Science and Technology Zhejiang University Hangzhou315000 China Department of Pathology Renmin Hospital of Wuhan University Wuhan430060 China

ISBN: (纸本)9789811613531

Drug-disease association depicts the accessibility landscape of the association at the molecular level, thereby revealing the intermolecular reaction in the process of drug reposition. However, the scarcity of drug-disease association data often complicates the drug reposition. In this work, we present a method for predicting drug-disease association, which is called drug-disease association prediction via Gaussian interaction profile kernel and long short-term memory network (G-LSTM). G-LSTM combines a recurrent network framework with a Gaussian interaction profile kernel to learn underlying features that accurately characterize drug-disease-association data. We have verified G-LSTM models on the two benchmark datasets Fdataset and Cdataset, respectively. The experimental results show that the predictive performance was significantly improved, with AUC of 0.9527 and 0.9705, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Chinese image Captioning via Fuzzy Attention-based DenseNet-BiLSTM

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2021年第1期17卷 1–18页

作者： Lu, Huimin Yang, Rui Deng, Zhenrong Zhang, Yonglin Gao, Guangwei Lan, Rushi Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc 1 Jinji Rd Guilin Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu Fukuoka Japan Nanjing Univ Posts & Telecommun Inst Adv Technol 9 Wenyuan Rd Nanjing Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model's ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU@1, BLEU@4, METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

关键词： image captioning fuzzy attention DenseNet BiLSTM

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Source-free Unsupervised Domain Adaptation for Privacy-Preserving intelligent Fault Diagnosis

Source-free Unsupervised Domain Adaptation for Privacy-Prese...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Mengliang Zhu Xiangyu Zeng Jie Liu Kaibo Zhou MOE Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Huazhong University of Science and Technology Wuhan P. R. China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan P. R. China

Rolling bearing is of vital significance in industrial applications and intelligent fault diagnosis (IFD) have been widely exploited in this field. However, cross-machine variations hinder the model performance across different machines in varying working conditions. To solve this issue, many unsupervised domain adaptation (UDA) approaches have been proposed recently, requiring direct access to the fully labeled source domain. However, privacy concerns are raised in traditional UDA scenario, since raw signals contain various private information. To address this issue, the source-free unsupervised domain adaptation (SFUDA) scenario is considered, where only the pre-trained source model is required, instead of the fully labeled source domain. In this paper, a SFUDA approach for intelligent fault diagnosis (IFD) is proposed. It consists of two steps: 1) source model generalization, where virtual adversarial training, R-Drop and label smoothing techniques are adopted to improve the generalization ability of the source model; and 2) target model adaptation, where information maximization is used for cluster assumption and mean teacher training paradigm is utilized to alleviate the catastrophic forgetting phenomenon. The proposed approach is verified on the Case Western Reserve University (CWRU) dataset. Experimental results on the show that the proposed SFUDA approach outperforms several typical UDA approaches, and its effectiveness is verified.

关键词： Fault diagnosis Training Employee welfare Adaptation models Privacy Smoothing methods Data analysis

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Exploiting Distilled Learning for Deep Siamese Tracking

Exploiting Distilled Learning for Deep Siamese Tracking

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International Conference on Pattern Recognition

作者： Chengxin Liu Zhiguo Cao Wei Li Yang Xiao Shuaiyuan Du Angfan Zhu Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Queen Mary University of London

Existing deep siamese trackers are typically built on off-the-shelf CNN models for feature learning, with the demand for huge power consumption and memory storage. This limits current deep siamese trackers to be carried on resource-constrained devices like mobile phones, given factor that such a deployment normally requires cost-effective considerations. In this work, we address this issue by presenting a novel Distilled Learning Framework(DLF) for siamese tracking, which aims at learning tracking model with efficiency and high accuracy. Specifically, we propose two simple yet effective knowledge distillation strategies, denote as point-wise distillation and pairwise distillation, which are designed for transferring knowledge from a more discriminative teacher tracker into a compact student tracker. In this way, cost-effective and high performance tracking could be achieved. Extensive experiments on several tracking benchmarks demonstrate the effectiveness of our proposed method.

关键词： Power demand Pipelines Memory management Benchmark testing Mobile handsets Pattern recognition

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