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Identification of Rice Species and Adulteration Using Gas Chromatography-ion Mobility Spectrometry and Multi-classification Maximum-interval Twin Support Vector Machine 3

Identification of Rice Species and Adulteration Using Gas Ch...

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Lian, Feiyu Fu, Maixia College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou450001 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Gas chromatography-ion mobility spectrometry (GC-IMS) was applied to identify rice varieties and adulteration in order to address the inefficiencies caused by the dependence on intricate biochemical procedures for the identification of rice species and *** original migration profile data for the identification of rice varieties and adulteration were provided by testing the volatile flavor substances of 5 kinds of rice. The original data was compressed through principal component analysis (PCA), which was then used as the classifier's input. An improved multi-classification maximum-interval twin support vector machine (MMTSVM) was proposed to construct a multi-classifier with increased classification efficiency and accuracy compared to the conventional support vector machine. The experimental results on the testing set demonstrated that the accuracy of rice variety identification was close to 94.00%, which was obviously better than that of conventional support vector machines, decision trees, and other models. At the same time, the experimental results also showed that the accuracy of the GC-IMS method inidentifying adulterated rice reached 93.3%, outperforming the traditional chromatographic or spectral analysis methods. © 2023 SPIE.

关键词： Principal component analysis

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Energy-Efficiency Resource Allocation for D2D Communications Under-Laying UAV Networks 13th

Energy-Efficiency Resource Allocation for D2D Communications...

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13th International Conference on Computer Engineering and Networks, CENet 2023

作者： Zhao, Pan Chen, Liuyuan Jiang, Zhiliang Han, Ming Yuan, Fucai Li, Aocheng Jia, Xiangyuan Fu, Xinqi Han, Jingqi Han, Jiaqi College of Mechanical and Electrical Engineering Henan University of Technology Zheng Zhou450001 China Henan Aerospace Hydraulic and Pneumatic Technology Co. Ltd. Zhengzhou China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou China Key Laboratory of Henan Province for Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China School of Applied Science Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9789819992386

Device-to-Device (D2D) communications under-laying Unmanned aerial vehicle (UAV) with its mobility extend the coverage and improve system capacity and flexible networking capabilities. However, the energy-efficiency are major challenge in UAV-assisted D2D networks. In this paper, the joint power and channel optimization problem has been investigated to maximize the system energy efficiency. Using the properties of objective function, the original optimization problem is transformed into two sub-problems. In the first step, the optimal power allocation problem is solved by using the Nash balance point of non-cooperative Game theory. In the second step, the channel allocation problem is constructed into a Bipartite graph, and then the Hungarian algorithm is used to find the best matching. The numerical simulation results show that the proposed scheme not only improves the energy efficiency of the system but also enhances the spectral efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Energy efficiency

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A Method for Segmenting grain Particles in Hyperspectral Images

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IEEE Sensors Journal 2025年第10期25卷 17617-17630页

作者： Yang, Zhen Niu, Yuhang He, Tingting Jiang, Huawei Zhao, Like Ministry of Education Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology Zhengzhou450001 China Ministry of Education Zhengzhou450001 China Advanced Laser Technology Laboratory of Anhui Province Hefei230000 China

Hyperspectral imaging, known for its high spectral resolution and nondestructive detection characteristics, has been widely applied in grain quality evaluation. grain quality evaluation utilizing near-infrared hyperspectral images often uses individual particles as the evaluation unit and thus relies on the segmentation of particles. However, widely applicable methods for the segmentation of grain particles remain inadequate because of the challenge of segmenting adhesive particles. Therefore, this study introduces a new method designed to significantly improve the accuracy of segmenting grain particles. This method considers the shape characteristics of grains to correct the oversegmentation in watershed segmentation caused by adhesive particles. The feasibility of the method was tested by applying it to three grains: corn, wheat, and peanuts. Results demonstrated that the new method performed significantly better than watershed segmentation, with accuracies of no less than 90% for the three grains. The new method has the potential to support various studies related to grain quality evaluation, such as mold identification and moisture detection of individual particles. © 2001-2012 IEEE.

关键词： Image segmentation

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Research on Explanation of CAM-Based CNNs 6

Research on Explanation of CAM-Based CNNs

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Yang, Shuzhi Zhu, Chunhua Li, Xue Ministry of Education Zhengzhou China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China Henan Engineering Laboratory of Grain Condition Intelligent Detection and Application Henan University of Technology Zhengzhou China

ISBN: (纸本)9798350374315

Class activation mapping (CAM) is a post-hoc explanation method for Convolutional neural networks (CNNs), aiming to visually explain the decision-making process of CNNs. CAM demonstrates superior categorical discrimination capabilities compared to visual explanation methods rooted in backpropagation and perturbation. Five CAM methods have been comprehensively analyzed and discussed, revealing their applications in explaining CNN decision-making. Additionally, the evaluation principles and metrics for CAM and other explainable methods are summarized. A comparative analysis highlights their characteristics, applicability, and inherent limitations. Finally, the challenges faced by CAM methods in the field of explainable research are explored, and future development trends are anticipated. © 2024 IEEE.

关键词： Convolutional neural networks

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Beamspace Channel Estimation Via PARAFAC Decomposition for RIS Assisted Millimeter-Wave Multiuser MISO Communications

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IEEE Transactions on Vehicular technology 2024年第5期74卷 7594-7609页

作者： Guo, Xinying Xie, Zongyuan Zhang, Jiankang Chen, Sheng Zhu, Chunhua Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing & Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

The reconfigurable intelligent surface (RIS) with massive low-cost passive reflecting elements integrated on a planar surface has the ability to favourably reconfigure the wireless propagation environment, thereby significantly improving the performance of wireless communication networks. In this work, we consider uplink (UL) channel estimation for the RIS assisted millimeter-wave multiuser multiple-input single-output beamspace system where the base station (BS) is equipped with lens antenna array. This channel state information (CSI) estimation task is extremely challenging for two reasons. First, the BS only has limited number of radio frequency chains but the size of beamspace channel is very large. Second, the number of passive components in the RIS is abundance but they lack signal processing capabilities. By exploiting the parallel factor (PARAFAC) decomposition of the received signals, we derive an iterative estimation algorithm, called unitary approximate message passing (UAMP), to accurately estimate the channels between the BS and the RIS as well as the channels between the RIS and the users. To guide the selection of the system parameters, we provide the uniqueness conditions for our PARAFAC decomposition based channel estimation. To theoretically verify the efficiency of our UAMP algorithm, the Cramér-Rao bound (CRB) of the estimation is also derived. Besides, we investigate the achievable downlink (DL) sum rate for the channel estimation obtained by the proposed algorithm by using the maximum power beam selection, the optimized phase shift matrix and the zero forcing precoding. Extensive simulation results demonstrate the excellent mean squared error (MSE) performance of our UAMP estimation algorithm. In particular, for sufficiently high UL signal-to-noise ratio, the MSE of our channel estimation reaches the CRB. Simulation results also show that the DL sum rate achieved by the estimated CSI is very close to that obtained by the perfect CSI. Theoretica

关键词： Mean square error

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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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Infrared Dim Small Target Detection Algorithm Based on Adaptive Attention Transformer Network

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Journal of Shanghai Jiaotong university (Science) 2025年 1-13页

作者： Ding, Leqi Xia, Yilin Wang, Biyun Cai, Yunze Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China National Key Laboratory of Air-based Information Perception and Fusion Henan Luoyang471009 China Shanghai Institute of Aerospace Control Technology Infrared Detection Technology R & D Center of China Aerospace Science and Technology Corporation Shanghai201109 China

This paper presents a novel algorithm for detecting infrared dim small targets in complex backgrounds by integrating both global and local feature information. The proposed method employs an adaptive attention transformer network which addresses challenges associated with computational complexity and information redundancy in traditional self-attention mechanisms. A global context aggregation module is designed to address the loss of small target features in high-level feature maps. This module compensates for spatial distribution information during the multi-scale feature fusion process, ensuring the preservation of target features. Comparison experiments were conducted on two datasets, NUAA-SIRST and IRSTD-1k. The proposed algorithm achieved 0.784 for mIoU and 0.878 for F-measure on NUAA-SIRST, 0.632 for mIoU and 0.807 for F-measure on IRST-1k, all of which are the highest values. The proposed algorithm shows a significant improvement over other existing detection algorithms, demonstrating its capability to accurately extract both local and global features of targets with high recall rates and low false alarm rates across various scenarios. The ablation study results highlight the significant improvements contributed by the key modules to the proposed algorithm. (Figure presented.) © Shanghai Jiao Tong university 2025.

关键词： A adaptive attention global context aggregation module infrared dim small target detection TP391.4 Transformer network

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Mitigation of polycyclic aromatic hydrocarbons(PAHs)in roasted beef patties by cold plasma treatment and products quality evaluation

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Food Science and Human Wellness 2024年第5期13卷 2993-3005页

作者： Yuke Hou Yangjian Hu Min Li Jiahui Nong Fengyuan Xie Yuhan Fan Jianhao Zhang Xianming Zeng Minyi Han Xinglian Xu Xia Wang Key Laboratory of Animal Products Processing Ministry of AgricultureKey Laboratory of Meat Processing and Quality ControlMinistry of EducationJiangsu Synergetic Innovation Center of Meat Production and ProcessingCollege of Food Science and TechnologyNanjing Agricultural UniversityNanjing 2100095China Center for Food and Drug Safety Inspection of Maanshan Maanshan Administration for Market RegulationMaanshan 243000China Henan Key Laboratory of Cold Chain Food Quality and Safety Control Zhengzhou University of Light IndustryZhengzhou 450001China Collaborative Innovation Center for Modern Grain Circulation and Safety Key Laboratory of Grains and Oils Quality Control and ProcessingCollege of Food Science and EngineeringNanjing University of Finance and EconomicsNanjing 210023China

The cold plasma(CP)technique was applied to alleviate the contamination of polycyclic aromatic hydrocarbon(PAH)in this *** different CP treatments methods were implemented in the production of beef patties,to investigate their inhibition and degradation capacity on *** 5 different cooking oils and fats addition,the inhibition mechanism of in-package cold plasma(ICP)pretreatment was explored from the aspect of raw patties fatty acids composition *** results of principal component analysis showed that the first two principal components accounted for more than 80%of the total variation in the original data,indicating that the content of saturated fatty acids was significantly positively correlated with the formation of *** pretreatment inhibited the formation of PAHs by changing the composition of fatty acids,which showed that the total amount of polyunsaturated fatty acids decreased and the total amount of monounsaturated fatty acids *** discrimination tests demonstrated there were discernable differences between 2 CP treated samples and the controls,utilization of the ICP pretreatment in meat products processing was expected to achieve satisfying eating *** conclusion,CP treatment degraded PAHs through stepwise ring-opening oxidation in 2 reported pathways,the toxicity of PAHs contaminated products was alleviated after CP treatment.

关键词： Roasted beef Polycyclic aromatic hydrocarbons Cold plasma Mitigation mechanism Product quality evaluation

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Fault-tolerant Hamiltonian cycle strategy for fast node fault diagnosis based on PMC in data center networks

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Mathematical Biosciences and Engineering 2024年第2期21卷 2121-2136页

作者： Zhao, Zhipeng Hu, Zhenyu Zhao, Zhiyu Du, Xiaoyu Chen, Tianfei Sun, Lijun Minister of Education Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China College of Engineering Economics Henan Finance University Henan Zhengzhou450001 China Luoyang Railway Station of China Railway Zhengzhou Bureau Group Co. LTD Henan Luoyang450001 China College of Computer and Information Engineering Henan University Henan Kaifeng475000 China

System-level fault diagnosis model, namely, the PMC model, detects fault nodes only through the mutual testing of nodes in the system without physical equipment. In order to achieve server nodes fault diagnosis in large-scale data center networks (DCNs), the traditional algorithm based on the PMC model cannot meet the characteristics of high diagnosability, high accuracy and high efficiency due to its inability to ensure that the test nodes are fault-free. This paper first proposed a fault-tolerant Hamiltonian cycle fault diagnosis (FHFD) algorithm, which tests nodes in the order of the Hamiltonian cycle to ensure that the test nodes are faultless. In order to improve testing efficiency, a hierarchical diagnosis mechanism was further proposed, which recursively divides high scale structures into a large number of low scale structures based on the recursive structure characteristics of DCNs. Additionally, we proved that 2(n−2)nk−1 and (n−2)tn,k/tn,1 faulty nodes could be detected for BCuben,k and DCelln,k within a limited time for the proposed diagnosis strategy. Simulation experiments have also shown that our proposed strategy has improved the diagnosability and test efficiency dramatically. © 2024 the Author(s), licensee AIMS Press.

关键词： Failure analysis

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ARTI: One New Adaptive Elliptical Weighting Model Combining with the Tikhonov-p-norm for Image Reconstruction 1

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12th International Conference on Broadband Communications, Networks, and Systems, BROADNETS 2021

作者： Zhu, Chunhua Shi, Zhen Yang, Weidong Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Henan Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Henan Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou450001 China

ISBN: (数字)9783030934798

ISBN: (纸本)9783030934781

To reconstruct the target-induced attenuation image keeping consistent with the observed measurement data, this paper explores the use of a new horizontal distance attenuation-based elliptical weighting model in building an attenuation image, where a horizontal distance attenuation factor and a vertical distance attenuation factor are introduced, respectively, which is able to clear the difference of the voxel weightings perpendicular to the line-of-sight (LOS) direction, as well as the difference of the voxel weightings parallel to the LOS direction. Compared with the existing model, the proposed model can additively reflect the occlusion effect of the radio frequency signal when the target is close to the transceiver nodes. Besides, the Tikhonov-p-norm regularization is incorporated into the image reconstruction, which makes full use of the sparse ability of the p-norm (0 © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Image reconstruction

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