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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Li, Longyan Ning, Chao Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

This paper proposes a novel uncertainty-aware energy management framework for Multi-energy Microgrid (MEMG), which comprehensively comprises electricity, heat, natural gas, hydrogen, and ammonia. In particular, green ammonia is produced from hydrogen, which is derived from electrolysis powered by renewable energy. The proposed framework seamlessly integrates day-ahead optimal scheduling with data-driven model predictive control. To offer a just-in-time resilience to uncertainties of renewable energy and load, we further develop event-triggered online learning distributionally robust model predictive control (ET-OLDRMPC). Specifically, an event trigger mechanism is designed to enable the controller to intelligently switch between certainty-equivalence and distributionally robust schemes as per their respective advantageous regimes, thereby ensuring operation safety while mitigating unnecessary conservatism. For the distributionally robust scheme, we leverage a nonparametric Bayesian model to construct online ambiguity sets of uncertainty distributions, which encode statistical multimodality and local moment information. The effectiveness of the proposed framework is validated in a case study. © 2022 IEEE.

关键词： Ammonia

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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Surrogate-Assisted Multiobjective Gene Selection for Cell Classification From Large-Scale Single-Cell RNA Sequencing Data

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 601-615页

作者： Lin, Jianqing He, Cheng Jiang, Hanjing Huang, Yabing Jin, Yaochu Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou310030 China

Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and computational time. Thus, we propose a surrogate-assisted evolutionary algorithm for multiobjective GS to address these deficiencies. An innovative two-phase initialization method is proposed to select sparse solutions to provide preliminary insights into gene contributions. Then, a binary competitive swarm optimizer is proposed for effective global search, where a local search method is embedded to eliminate irrelevant genes for efficiency consideration. Additionally, a surrogate model is adopted to forecast classification accuracy efficiently and substitutes part of the computationally expensive classification process. Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20 000 genes. The effectiveness of the proposed GS method for scRNA-seq cell classification compared with eight state-of-the-art methods is validated. Gene expression analysis results of selected genes further validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data. © 1997-2012 IEEE.

关键词： Gene expression

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Efficient dynamic fusion for multispectral pedestrian detection 12

Efficient dynamic fusion for multispectral pedestrian detect...

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SPIE 12th International Symposium on Multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Cao, Anqi Li, Haipeng Yang, Jian Mi, Xiaofei Chen, Zhong State Key Laboratory for Multispectral Information Processing Technology Huazhong University of Science and Technology Wuhan Hubei China Beijing Aerospace Automatic Control Institute Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

When in an environment with insufficient light, RGB images cannot provide clear pedestrian information, while multispectral thermal imaging can provide accurate positioning information. Therefore, multispectral object detection is more reliable and robust in the open world. Most fusion methods for existing multispectral object detection are static, that is, after the network is trained, all the modalities of each piece of data are input into the network to perform static inference. However, when the light is good enough or the light is very dark, multi-spectral input will bring unnecessary noise interference and computational redundancy. Therefore, a dynamic fusion network (EDFNet) is proposed to selectively fuse RGB and thermal data, so that the network can efficiently perform multispectral fusion and improve the accuracy of object detection. The gating function included in the fusion network is able to perform modality-level decisions based on multimodal features, enabling dynamic inference on data. The Extensive experiments on multiple datasets demonstrate that the proposed fusion method can reduce the computation costs and obtain comparable detection performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Object detection

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Discrimination of wheat unsound grains based on deep learning and terahertz spectral image technology 17

Discrimination of wheat unsound grains based on deep learnin...

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17th National Conference on Laser Technology and Optoelectronics

作者： Wang, Fei Jiang, Yuying Ge, Hongyi Chen, Xinyu Li, Li Key Laboratory of Grain Information Processing & Control Ministry of Education Henan University of Technology Zhengzhou450001 China

ISBN: (纸本)9781510661097

The problem of insignificant image features and poor image quality in the acquisition of terahertz spectral images of unsound wheat. Therefore, this study proposed the FFDNet-VGG terahertz image enhancement algorithm based on the FFDNet denoising algorithm and VGG feature extraction algorithm. The experimental results showed that this algorithm could effectively improve the image quality compared with the traditional algorithms such as BM3D and WNNM, and the PSNR and SSIM of the enhanced images were 39.10 dB and 0.93, respectively. The wheat unsound grain images processed by FFDNet, VGG and FFDNet-VGG algorithms were verified by CNN classification network with the classification accuracy of 93.1% and 93.7%, respectively. FFDNet-VGG effectively enhances the wheat unsound grain terahertz spectral images. © 2022 SPIE.

关键词： Image denoising

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FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI Segmentation in Incomplete Multi-Modal Scenarios 24

FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI...

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7th International Conference on Image and Graphics processing, ICIGP 2024

作者： Yang, Rui Wang, Xiao Xu, Xin Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan China

ISBN: (纸本)9798400716720

Magnetic Resonance Imaging (MRI) is an invaluable tool for brain tumor segmentation. However, in clinical practice, certain modalities might be unavailable, leading to potential performance degradation in prediction tasks. According to current implementations, different modalities are treated as independent entities during the feature extraction phase, ignoring their inherent complementary nature. In this paper, inspired by knowledge distillation techniques, we introduce the Fusion-Inspired Modality Distillation (FIMD) framework. FIMD leverages the output from a fused multi-modal model as a teacher to guide the training of individual modalities. Each modality benefits from the collective knowledge of all modalities, enhancing its performance. Our FIMD method, devoid of specific architectural constraints, seamlessly integrates into existing multi-modal brain tumor segmentation frameworks. Notably, extensive experiments on BraTS2020, BraTS2018, and BraTS2015 datasets indicate that FIMD can enhance the performance of current state-of-the-art (SOTA) algorithms on Dice by an average of 0.55-1.60%. Especially in the case of missing modalities, the effectiveness of the FIMD framework in enhancing the robustness and accuracy of brain tumor segmentation is demonstrated. © 2024 ACM.

关键词： Semantic Segmentation

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Streaming-Data-Driven Multi-Objective Optimal Operation for Ammonia-based Integrated Energy Chemical systems: An Event-Triggered Scheme 42

Streaming-Data-Driven Multi-Objective Optimal Operation for ...

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42nd Chinese control Conference, CCC 2023

作者： Li, Longyan Ning, Chao Shanghai Jiao Tong University Department of Automation Shanghai200240 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9789887581543

As an important component in the industry sector, the integrated energy chemical system is inescapably responsible for carbon reduction in addition to economic and reliability performance. We propose a novel streaming-data-driven multi-objective optimal operation for ammonia-based integrated energy chemical systems (AIECS). In the day ahead scheduling, a detailed mathematical model for AIECS is presented, upon which a multi-objective optimization considering cost, CO2 emission and reliability is established. For the intra-day operation, an event-triggered mechanism is firstly developed in the real-time operation of AIECS, and it enables the AIECS to be risk-aware just in time. By monitoring the difference between the predicted state of charge and the capacity limitations of battery, the AIECS identifies whether it is in hazardous regime. Then, the intelligent switch between certainty equivalence model predictive control and online-learning based distributionally robust model predictive control is proposed, such that their respective advantages are leveraged. Case studies demonstrate that the proposed approach guarantees the system safety, and brings good revenue. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Model predictive control

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Data-Driven Distributionally Robust Planning of Energy Hub Coupled with Carbon Capture Utilization and Storage Facilities 42

Data-Driven Distributionally Robust Planning of Energy Hub C...

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42nd Chinese control Conference, CCC 2023

作者： Ma, Xutao Ning, Chao Shanghai Jiao Tong University Department of Automation Shanghai200240 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9789887581543

Toward carbon peaking and carbon neutrality goals in China, a transition from traditional fossil power to renewable energy becomes an imperative need. This paper aims to address the planning problem of a highly renewable-energy penetrated energy hub system, coupled with carbon capture facilities and downstream production processes of hydrogen and methanol, under the uncertainty of renewable power output. To this end, we propose a two-stage distributionally robust optimal planning model, where investment decisions are the first-stage variables and operation variables are determined at the second stage after the realization of uncertainty. In the proposed model, we utilize historical data samples to construct a strengthened ambiguity set that leverages both Wasserstein distance and moment information for a better characterization of renewable generation uncertainty. Moreover, to efficiently solve the proposed planning problem, we develop a simplified affine decision rule method in a general setting with both moment and Wasserstein ambiguity, and then apply it to reformulate the distributionally robust planning problem. The derived reformulation possesses the favorable property that its scale is free from the sample size, thus greatly reducing the computational complexity. Computational results validate the effectiveness of the proposed planning model and the computational efficiency of the solution methodology. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Carbon capture

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Synthesis of Failure-Robust Plans for Multi-Robot systems Under Temporal Logic Specifications

Synthesis of Failure-Robust Plans for Multi-Robot Systems Un...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Feifei Huang Shaoyuan Li Xiang Yin Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

In this study, we address the multi-robot path planning problem for tasks specified by linear temporal logic (LTL) formulae. Unlike existing studies, we take into account the possibility of robot failures, where a failed robot can no longer contribute to the completion of the LTL task. Our objective is to find a failure-robust path, which ensures that the LTL task can always be fulfilled, even if a maximum number of robots fail at any point during execution. To achieve this, we extend the mixed-integer linear programming (MILP) approach to the failure-robust setting. To overcome the computational complexity, we identify a fragment of LTL formulae called the free-union-closed LTL, which allows for more scalable synthesis without considering the global combinatorial issue. We present case studies to demonstrate our findings. Our approach provides a novel solution to the problem of multi-robot path planning under robot failures, offering a practical and efficient way to achieve robustness in the face of unforeseen events.

关键词：

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Multi-Scale and Improved Skip Connections Network for Skin Lesion Segmentation 9

Multi-Scale and Improved Skip Connections Network for Skin L...

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9th International Conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Zhang, Botao Chen, Jun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9798350376548

To solve the problem that the current skin lesion segmentation networks have a poor segmentation accuracy when lesion areas are of different shapes and sizes and low contrast at the edge areas, based on U-Net, we present a skin lesion segmentation network based on multi-scale and improved skip connections. Firstly, a multi-scale feature enhancement module is designed to replace the U-Net's 3× 3 convolution blocks, which can improve the feature extraction ability of the network about lesion areas of different shapes and sizes. Secondly, a lesion attention module is introduced into the skip connection to improve the semantic expression of low-level features and the location attention of high-level features, which can improve the fine-grained information transmission efficiency at the skip connection. Finally, a new hybrid loss function is designed to calculate the label and the edge loss of lesion areas, which improves the ability of the network to segment lesion areas and edges. Experiments are conducted on the ISIC2017 and ISIC2018 datasets, and the results prove that our method have a higher segmentation accuracy in skin lesion segmentation. © 2024 IEEE.

关键词： Problem solving

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