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arXiv 2020年

作者： Cui, Yuqi Wang, Huidong 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 School of Management Science and Engineering Shandong University of Finance and Economics Jinan250014 China

Fuzzy c-means based clustering algorithms are frequently used for Takagi-Sugeno-Kang (TSK) fuzzy classifier antecedent parameter estimation. One rule is initialized from each cluster. However, most of these clustering algorithms are unsupervised, which waste valuable label information in the training data. This paper proposes a supervised enhanced soft subspace clustering (SESSC) algorithm, which considers simultaneously the within-cluster compactness, between-cluster separation, and label information in clustering. It can effectively deal with high-dimensional data, be used as a classifier alone, or be integrated into a TSK fuzzy classifier to further improve its performance. Experiments on nine UCI datasets from various application domains demonstrated that SESSC based initialization outperformed other clustering approaches, especially when the number of rules is small. Copyright © 2020, The Authors. All rights reserved.

关键词： Fuzzy clustering

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A protocol for structured illumination microscopy with minimal reconstruction artifacts

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Biophysics Reports 2019年第2期5卷 80-90页

作者： Junchao Fan Xiaoshuai Huang Liuju Li Shan Tan Liangyi Chen Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China School of Automation Huazhong University of Science and Technology State Key Laboratory of Membrane Biology Beijing Key Laboratory of Cardiometabolic Molecular MedicineInstitute of Molecular Medicine Peking University

The imaging rate of structured illumination microscopy(SIM) reached 188 Hz recently. As the exposure time decreases, the camera detects fewer virtual photons, while the noise level remains the same. As a result, the signal-to-noise ratio(SNR) decreases sharply. Furthermore, the SNR decreases further because of photobleaching and phototoxicity. This decreased quality of SIM raw data may lead to surprising artifacts with various causes, which may confuse a new user of SIM microscopy. We summarize three significant possible sources of severe artifacts in reconstructed super-resolution(SR)images. Ultrafast motion of a biological sample or an uneven illumination pattern is the most difficult to be identified. The estimated parameter could also be incorrect, leading to artifact of regular ***, reconstruction with the Wiener method generates stochastic artifacts due to the amplification of noise during the deconvolution process. To deal with these problems, we have established a protocol to reconstruct ultrafast SIM raw data obtained in low SNR conditions. First, we checked the quality of the raw data with the image J plugin SIMcheck before reconstruction. Then, a modified parameter estimation method was used to improve the precision of the parameters. Finally, an iterative algorithm was used for SIM reconstruction under low signal-to-noise ratio conditions. This procedure effectively suppressed the artifacts in the super-resolution images reconstructed from raw data of low signal-to-noise ratio.

关键词： Ultrafast SIM Low signal-to-noise ratio Reconstruction

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Camouflaged Object Detection via Context-aware Cross-level Fusion

arXiv

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arXiv 2022年

作者： Chen, Geng Liu, Si-Jie Sun, Yu-Jia Ji, Ge-Peng Wu, Ya-Feng Zhou, Tao Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering China Data Processing Center School of Power and Energy China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science Inner Mongolia University Hohhot010021 China Artificial Intelligence Institute School of Computer Science Wuhan University Wuhan430072 China

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare C2F-Net with state-of-the-art (SOTA) models. The results show that C2F-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our C2F-Net in COD downstream applications. Our code is publicly available at: https://***/Ben57882/C2FNet-TSCVT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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BoostTree and BoostForest for Ensemble Learning

arXiv

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arXiv 2020年

作者： Zhao, Changming Wu, Dongrui Huang, Jian Yuan, Ye Zhang, Hai-Tao Peng, Ruimin Shi, Zhenhua 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 Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The Autonomous Intelligence Unmanned Systems Engineering Research Center of Ministry of Education of China The State Key Lab of Digital Manufacturing Equipment and Technology Wuhan China

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This paper proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Multi-stage hybrid multiobjective evolutionary algorithm for multi-type vehicle routing problem with simultaneous pickup and delivery and time windows

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Computers & Industrial Engineering 2025年 207卷

作者： Wenqiang Zhang Shun Li Miaolei Deng Yashuang Mu Peng Li Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Ministry of Education China Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology China School of Artificial Intelligence and Big Data Henan University of Technology China College of Information Science and Engineering Henan University of Technology China Institute for Complexity Science Henan University of Technology China

The vehicle routing problem (VRP) is a fundamental and extensively studied problem in logistics and transportation. Despite its significance, real-world applications often require more complex variants to address specific operational constraints and objectives. To address these limitations, this paper introduces the multi-type vehicle routing problem with simultaneous pickup and delivery and time windows (MTVRPSPDTW). Although hybrid multiobjective algorithms have been successful in combination optimization problems in recent years, it is still challenging to improve the performance of the algorithms by combining them with different timing. We propose a multi-stage hybrid evolutionary multi-objective optimization with a multi-region sampling strategy (MS-HEMO-MRSS) to optimize both vehicle number and wait time of MTVRPSPDTW. The algorithm integrates a three-stage hybrid approach, combining a global search using the multi-region sampling strategy (MRSS) with a local search based on routing sequence differential evolution (RSDE). The initial stage employs MRSS to quickly position the population near the Pareto front from various directions. The second stage utilizes RSDE to accelerate convergence towards central and edge areas of the Pareto front. In the final stage, individuals on Pareto front are selected and RSDE is used again to guide them towards the edge regions to enhance distribution performance. Specialized encoding, decoding techniques, and genetic operators tailored for two vectors are introduced to optimize MTVRPSPDTW. Comparative experiments with traditional multiobjective evolutionary algorithms demonstrate significant convergence and notable distribution performances, highlighting the benefits of employing different optimization strategies at different evolutionary stages.

关键词： Pareto principle

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Classification of transgenic corn varieties using terahertz spectroscopy and convolutional neural network

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Journal of Food Composition and Analysis 2025年 145卷

作者： Jiang, Yuying Wen, Xixi Ge, Hongyi Li, Guangming Chen, Hao Jiang, Mengdie Sun, Qingcheng Wei, Shilei Li, Peng Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou 450001 China Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou 450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou 450001 China School of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Institute for Complexity Science Henan University of Technology Zhengzhou 450001 China

Corn is the largest grain crop in China. With the increasing use of transgenic corn in agricultural production, accurately identifying transgenic corn varieties has become crucial for ensuring food safety and quality. This study combines terahertz time-domain spectroscopy (THz-TDS) technology with a convolutional neural network (CNN) to classify five transgenic corn varieties. First, the time-domain spectra of transgenic corn were collected, and the frequency-domain spectra and absorption coefficient spectra were calculated. Next, abnormal data were removed using the isolation forest algorithm. The data were then preprocessed using the first derivative (FD) and principal component analysis (PCA) to extract effective spectral features. Based on these features, classification models were constructed using support vector machine (PCA-SVM), grid search optimized SVM (PCA-GS-SVM), particle swarm optimization SVM (PCA-PSO-SVM), and hippopotamus optimization SVM (PCA-HO-SVM). Additionally, a one-dimensional CNN (1D-CNN) model was built using the full-band spectral data. The results indicate that the performance of the 1D-CNN model with FD preprocessing was significantly improved, with an accuracy of 96.68 % and a misclassification rate of only 3.32 %. The study effectively classifies transgenic corn with high accuracy, showing potential for widespread application in quality detection. © 2025 Elsevier Inc.

关键词： Classification Convolutional neural network Support vector machine Terahertz spectroscopy Transgenic corn

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Corrigendum to “Classification of transgenic corn varieties using terahertz spectroscopy and convolutional neural network” [J. Food Compos. Anal. 145 (2025) 107771]

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Journal of Food Composition and Analysis 2025年

作者： Yuying Jiang Xixi Wen Hongyi Ge Guangming Li Hao Chen Mengdie Jiang Qingcheng Sun Shilei Wei Peng Li Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou 450001 China Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou 450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou 450001 China School of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Institute for Complexity Science Henan University of Technology Zhengzhou 450001 China

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Bioimage-based protein subcellular location prediction： a comprehensive review

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Frontiers of Computer Science 2018年第1期12卷 26-39页

作者： Ying-Ying XU Li-Xiu YAO Hong-Bin SHEN Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasingly popular in automatically analyzing the protein subcellular location pat- terns. Compared with the widely used protein 1D amino acid sequence data, the images of protein distribution are more intuitive and interpretable, making the images a better choice at many applications for revealing the dynamic char- acteristics of proteins, such as detecting protein translocation and quantification of proteins. In this paper, we systemati- cally reviewed the recent progresses in the field of automated image-based protein subcellular location prediction, and clas- sified them into four categories including growing of bioim- age databases, description of subcellular location distribution patterns, classification methods, and applications of the pre- diction systems. Besides, we also discussed some potential directions in this field.

关键词： bioimage informatics protein subcellular loca-tion prediction global and local features multi-location pro-tein recognition

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A Multi-Criteria Optimal Operation Framework for Renewable Energy Integrated Data Center Microgrid with Waste Heat Recovery

A Multi-Criteria Optimal Operation Framework for Renewable E...

引用

Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： Yuanzheng Li Jingjing Huang Yun Liu Hao Wang Yongzhen Wang Xiaomeng Ai School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China School of Electric Power Engineering South China University of Technology Guangzhou China Monash University Melbourne VIC Australia Energy Internet Research Institute Tsinghua University Beijing China School of Electrical and Electronics Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781728186337

The rapid development of data centers (DCs) leads to a huge challenge in their energy consumption and environmental impact. It is promising to establish data center microgrid (DCMG) with renewable energy generation and waste heat recovery system to address these issues. However, the efficient energy management of DCMG is to be pursued. In this paper, we propose a multi-criteria optimal operation framework for the DCMG, considering coordinated scheduling of energy supply and demand. In this framework, multiple criteria are comprehensively considered via a multi-criteria optimization (MCO) approach. Then, we adopt the augmented e-constraint algorithm to address the corresponding MCO problem. Afterwards, a balanced decision making method is further proposed to determine the optimal scheduling solution. Finally, case study and results analysis verify the effectiveness of proposed multi-criteria optimal operation framework for the DCMG.

关键词： Data centers Renewable energy sources Uncertainty Supply and demand Simulation Decision making Optimal scheduling

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RGB-based combustion state classification and stability analysis of SOFC afterburner: effects of fuel utilization and steam-to-carbon ratio

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Fuel 2025年 402卷

作者： Jingjing Wang Shuyu Zhang Jingang Lai Jiashu Jin Chun Zou Jung-Sik Kim Zhonghua Deng Yuanwu Xu Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Coal Combustion Huazhong University of Science and Technology Wuhan 430074 China Sino-French Engineer School/School of General Engineering Beihang University Beijing 100191 China Wuhan Huamao Automation Co. Ltd. Wuhan 430205 China School of Artificial Intelligence and Automation Wuhan University of Science and Technology Wuhan 430081 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518055 China

Solid oxide fuel cell (SOFC) systems efficiently convert chemical energy into electricity but produce high-temperature exhaust gases containing unreacted fuels. The afterburner combusts these residual fuels to enhance heat recovery and system efficiency, with its performance influenced by stack fuel composition variations. This study investigates how fuel utilization rate ( U f ) and steam-to-carbon ratio (S/C) affect afterburner combustion characteristics in a 1 kW methane-reformed SOFC system. Results showed maximum afterburner temperature decreased from 981.3 K to 835 K as U f increased to 0.6, while S/C minimally impacted temperature distribution. A dimensionless stability index was developed to quantitatively assess combustion performance. An image-based analysis approach extracted five key parameters describing flame characteristics under varying conditions. These parameters supported the development of a three-tier hierarchical classification framework to identify weak, stable, and unstable combustion states, achieving an overall recognition accuracy of 74.5 %. These findings provide insights into SOFC operating parameters, residual stack fuel compositions, and afterburner stability relationships. The proposed assessment methodology offers guidance for optimizing thermal management and improving energy recovery efficiency in SOFC systems.

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