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63rd IEEE Conference on Decision and control, CDC 2024

作者： Yu, Chang Lu, Xiaoqing Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China Huazhong University of Science and Technology Ministry of Education Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China Wuhan University School of Electrical Engineering and Automation Wuhan430000 China

ISBN: (纸本)9798350316339

Microgrids subjected to secondary cooperative control encounter significant challenges, including operational constraints and clock drifts, adversely affecting their stability and efficiency. This paper provides conditions that assure optimal microgrid performance in both transient and steady-state scenarios, focusing on the effects of clock drifts and fluctuations in load. Furthermore, we introduce a novel approach for designing secondary control parameters, specifically engineered to minimize steady-state discrepancies attributable to clock drifts while ensuring adherence to standards for transient operations. Comprehensive experimental validations corroborate the effectiveness of our proposed solutions. © 2024 IEEE.

关键词： Microgrids

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A novel segmentation method for convex lesions based on dynamic programming with local intra-class variance 12

A novel segmentation method for convex lesions based on dyna...

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27th Annual ACM Symposium on Applied Computing, SAC 2012

作者： Yu, Mali Huang, Qiliang Jin, Renchao Song, Enmin Liu, Hong Hung, Chih-Cheng School of Computer Science Huazhong University of Science and Technology Wuhan China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Wuhan Hubei China Radiology Department Second People's Hospital of Nanning Nanning China School of Computing and Software Engineering Southern Polytechnic State University GA United States

ISBN: (纸本)9781450308571

Lesion segmentation plays an important role in medical image processing and analysis. There exist several successful dynamic programming (DP) based segmentation methods for general images. In those methods, the gradient is used as an important factor in the cost function to attract the contours to the boundaries. Since medical images have their characteristics such as low contrast, blurred edges and high noises, the gradient operator cannot work well enough to achieve a satisfactory performance for boundary detection. We define the local intra-class variance and combine it with the dynamic programming method to replace the traditional gradient operation. Experiments on synthetic and X-ray images are carried out and the results are compared with Canny and fast multilevel fuzzy edge detection (FMFED) algorithms. It is demonstrated that the proposed method performs better on medical images with low contrast, blurred edges and high noises. In addition, 483 regions of interest of mammograms randomly extracted from DDSM of the University of South Florida are used to compare our proposed method with the plane-fitting and dynamic programming method (PFDP), and the normalized cut segmentation method (Ncut). The results demonstrate that our method is more accurate and robust than PFDP and Ncut. © 2012 ACM.

关键词： Dynamic programming

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A combining method for tumors detection from near-infrared breast imaging

A combining method for tumors detection from near-infrared b...

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2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005

作者： Wang, Zhicheng Liu, Jian Tian, Jinwen Xie, Zeping State Education Commission Key Laboratory for Image Processing and Intelligent Control Huazhong Univ. of Sci. and Tech. Wuhan 430074 China Institute for Pattern Recognition and Artificial Intelligence Huazhong Univ. of Sci. and Tech. Wuhan 430074 China School of Life Sci. and Tech. Huazhong Univ. of Sci. and Tech. Wuhan 430074 China

ISBN: (纸本)0780387406

This paper introduces the new qualitative and quantitative methods, which can diagnose breast tumors. Qualitative methods include blood vessel display inside and outside of pathological changes part of breast, display of equivalent pixel curves at the part of pathological changes and display of breast tumor image edge. Accordingly, three feature extraction operators are proposed, i.e. the combination operators of anisotropic gradient and smoothing operator, an improved Sobel operator and an edge sharpening operator. Furthermore, quantitative diagnose approaches are discussed based on blood and oxygen contents according to abundant clinical data and pathological mechanism of breast tumors. The results of clinic show that the methods of combining qualitative and quantitative diagnose are effective for breast tumor images, especially for early and potential breast cancer. © 2005 IEEE.

关键词： Tumors

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The method of image restoration in the environments of dust

The method of image restoration in the environments of dust

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International Conference on Mechatronics and Automation

作者： Wang, Yuanyu Li, Yuanzong Zhang, Tianxu Department of Computer Fundamental Education TaiYuan University of Technology TaiYuan CO 030024 China College of Mechanical Engineering TaiYuan University of Technology TaiYuan CO 030024 China Institute for Pattern Recognition and A.I. State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology WuHan CO 430074 China

ISBN: (纸本)9781424451418

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, a particle swarm optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with dual threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments. © 2010 IEEE.

关键词： Dust

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GraphMFT: A graph network based multimodal fusion technique for emotion recognition in conversation

arXiv

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arXiv 2022年

作者： Li, Jiang Wang, Xiaoping Lv, Guoqing Zeng, Zhigang Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems Wuhan430074 China

Multimodal machine learning is an emerging area of research, which has received a great deal of scholarly attention in recent years. Up to now, there are few studies on multimodal Emotion Recognition in Conversation (ERC). Since Graph Neural Networks (GNNs) possess the powerful capacity of relational modeling, they have an inherent advantage in the field of multimodal learning. GNNs leverage the graph constructed from multimodal data to perform intra- and inter-modal information interaction, which effectively facilitates the integration and complementation of multimodal data. In this work, we propose a novel Graph network based Multimodal Fusion Technique (GraphMFT) for emotion recognition in conversation. Multimodal data can be modeled as a graph, where each data object is regarded as a node, and both intra- and inter-modal dependencies existing between data objects can be regarded as edges. GraphMFT utilizes multiple improved graph attention networks to capture intra-modal contextual information and inter-modal complementary information. In addition, the proposed GraphMFT attempts to address the challenges of existing graph-based multimodal conversational emotion recognition models such as MMGCN. Empirical results on two public multimodal datasets reveal that our model outperforms the state-Of-The-Art (SOTA) approaches with the accuracy of 67.90% and 61.30%. Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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AN IKONOS image FUSION PROCESS USING SUPPORT VALUE TRANSFORM

AN IKONOS IMAGE FUSION PROCESS USING SUPPORT VALUE TRANSFORM

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： SHENG ZHENG WENZHONG SHI JIAN LIU Institute of Intelligent Vision and Image Information Best College of Electrical Engineering and In Advanced Research Center for Spatial Information Technology Department of Land Surveying and Geo-In State Education Commission Key Lab for Image Processing and Intelligent Control HUST Wuhan 430074

Panchromatic (Pan)-sharpening of multispectral (MS) bands is an important technique in various applications of satellite remote *** this paper, we apply the support value transform (SVT) to Ikonos image *** fused saliency features are represented by support values and extracted by *** low-resolution MS bands are resampled to the fine scale of the Pan image and sharpened by injecting the detailed features extracted from the high-resolution Pan *** fusing results on Ikonos MS + Pan data demonstrate that the proposed image fusion method is effective and efficient.

关键词： Mapped least squares support vector machine (mapped LS-SVM) support value transform (SVT) Ikonos image fusion

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Residential virtual power plant with photovoltaic output forecasting and demand response

Residential virtual power plant with photovoltaic output for...

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作者： Cui, Shichang Wang, Yan-Wu Lin, Xiangning Xiao, Jiang-Wen School of Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China Hubei Provincial Collaborative Innovation Center for New Energy Microgrid China Three Gorges University Yichang China State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan China

Decreasing conventional power supply is promoting the development of the distributed renewable energy sources, such as solar power and wind power. Recently rooftop photovoltaic has been widely applied, and accordingly efficient energy management is getting increasingly important for fully use of renewable energy and the peak shaving of the main grid. This paper investigates the residential energy management as a small-scale virtual power plant (VPP) connected to the main grid includes distributed energy resources, energy storages and residential loads. The self-organizing map (SOM) and the radical basis function (RBF) networks are adopted to classify the weather types and predict hourly photovoltaic output precisely. In a time-of-use electricity market, price-based demand response is applied to adjust the demand. The residential VPP has two goals: maximum profit by selling surplus power to grid and minimum power purchased from grid. The two goals are integrated as an optimization object by introducing a weight parameter. The algorithm combining receding horizon optimization and linear programming is proposed to solve the optimization problem in residential VPP. Numerical simulation tests can help to find the most suitable value of the weight parameter. Different scenarios are simulated and discussed to demonstrate the performance of the VPP and the proposed algorithm. © 2019 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Energy management

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Real-Time Maritime Obstacle Detection Based on YOLOv5 for Autonomous Berthing 16th

Real-Time Maritime Obstacle Detection Based on YOLOv5 for Au...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Chen, Guotong Qi, Jiangtao Dai, Zeyu Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China China Ship Scientific Research Center No. 222 Shanshui East Road Binhu District Wuxi214082 China

ISBN: (纸本)9789811912528

The shipping industry is developing towards intelligence, in which autonomous berthing and docking systems play an important role. Real-time maritime obstacle detection is essential for autonomous berthing, which can protect ships from collisions. In the field of real-time objection detection, YOLOv5s is one of the most effective networks. To solve the real-time maritime obstacle detection for autonomous berthing, we propose an improved network, named YOLOv5s-CBAM, which employs the convolutional block attention module to boost the representation power of YOLOv5s. In addition, we propose another network, named YOLOv5-SE, in which a squeeze-and-excitation block is introduced to recalibrate the channel-wise features adaptively. The experimental results demonstrate that YOLOv5s-CBAM outperforms YOLOv5s in the detection of large obstacles. Both YOLOv5s-CBAM and YOLOv5s-SE achieve state-of-the-art performance in various metrics compared with previous related work for maritime obstacle detection. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Ships

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Assignment-Driven Multi-consensus in Second-order Multi-agent Systems via Impulsive control with Heterogeneous delays

Assignment-Driven Multi-consensus in Second-order Multi-agen...

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IEEE International Conference on control and Automation

作者： Yao-Wen Wei Jing-Wen Yi Yan-Wu Wang Jiang-Wen Xiao School of Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education

ISBN: (纸本)9781509017393

An assignment-driven multi-consensus problem of multi-agent systems with heterogeneous delays is investigated in this paper. By introducing a concept of intelligent adjustment factor, a distributed impulsive protocol is designed, in which only the delayed sampled relative positions to neighbors and the relative position to the last sampling state are utilized. Combined with the characteristic of impulsive control, three steps of model transformation are performed, and the assignment-driven multi-consensus problem of the original continuous-time system is converted to the stability problem of a discrete-time expanded analogous error system. A necessary and sufficient criterion is derived to guarantee the assignment-driven multi-consensus of the multi-agent system. An interesting simulation is proposed to demonstrate the effectiveness of the theoretical result and reveal the effect of intelligent adjustment factors on the assignment-driven multi-consensus.

关键词： Protocols Delays Multi-agent systems Network topology Automation Stability criteria

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TMMM: Transformer in Multimodal Sentiment Analysis under Missing Modalities

TMMM: Transformer in Multimodal Sentiment Analysis under Mis...

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International Joint Conference on Neural Networks (IJCNN)

作者： Huanyu Wu Siyang Li Dongrui Wu 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

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The cross-task gap presents a significant challenge for multimodal models because of the differences in input-output workflows. For instance, multimodal pre-trained transformers may encounter uni-modal data during testing. To mitigate the gap, this paper introduces a Transformer in Multimodal Sentiment Analysis under Missing Modalities (TMMM) aims to perform well using missing-modal data during testing. TMMM uses a missing multimodal training approach to prevent accuracy degradation in testing. At the same time, a new network architecture allows the model to reconstruct missing modalities during testing. Classification token fusion and Mixture-of-Experts structures further enhance the model’s performance. A pre-training method utilizing contrastive learning, which can construct negative samples with positive samples, is proposed to overcome insufficient labeled data. Our experiments demonstrated the effectiveness of TMMM on two datasets with no modalities missing, i.e., it consistently achieved the highest classification accuracy and Macro-F1, which outperformed the best state-of-the-art baseline on each dataset by about 2% and 2.5%. Additionally, TMMM usually performs better than other baselines on datasets with missing modalities during testing.

关键词： Training Sentiment analysis Accuracy Contrastive learning Network architecture Transformers Brain modeling

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