检索结果-内蒙古大学图书馆

Prediction and influencing factors analysis of stored grain temperature and intergranular relative humidity

Food and Bioproducts processing 2025年

作者： Yifei Qin Yuan Zhang Shanshan Duan Yunhao Cao Xintao Jia Quanen Chen School of Mechanical and Electrical Engineering Henan University of Technology Zhengzhou 450001China Key Laboratory of Grain Information Processing and Control Henan Key Laboratory of Grain Photoelectric Detection and Control School of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Beijing Guomao Dongfu Engineering Technology Co. Ltd. Beijing 100037 China

To address the issues of insufficient accuracy and poor interpretability in the prediction of temperature and intergranular relative humidity (RH) for bulk-stored grain, this study developed an optimized prediction model based on the extreme gradient boosting (XGBoost) algorithm. The model incorporated the interaction of meteorological factors with the temperature and RH within the bulk grain. Hyperparameters were optimized using the grid search method, and Shapley Additive exPlanations (SHAP) was employed to quantify the contribution and interaction mechanism of each input variable, thereby enhancing model interpretability. Seven meteorological variables and the surface RH of the bulk grain were used to predict the surface temperature. Similarly, meteorological data and surface temperature were used to predict the surface RH, and the model performance was validated using an experimental silo dataset. For benchmarking purposes, several commonly used models, including Linear Regression (LR), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Support Vector Regression (SVR) were also tested. The results indicated that the XGBoost model outperformed the baseline models in predictive accuracy. The RMSE, R², MAE, and MAPE results for average surface temperature prediction were 1.59, 0.88, 0.79, 3.56%, respectively, while those for average surface RH were 0.32, 0.95, 0.16, 0.28%. SHAP analysis revealed that the influence of meteorological variables on grain surface temperature and intergranular RH was complex, with air temperature, air pressure, and air RH identified as the most significant factors. The proposed model demonstrated both high predictive accuracy and strong interpretability, offering a scientific basis for the monitoring and control of grain storage conditions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Deep spatiooral attention model for grain storage temperature forecasting 26

Deep spatiooral attention model for grain storage temperatur...

引用

26th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2020

作者： Duan, Shanshan Yang, Weidong Wang, Xuyu Mao, Shiwen Zhang, Yuan Henan Key Laboratory of Grain Photoelectric Detection Control Key Laboratory of Grain Information Processing Control College of Information Science Engineering Henan University of Technology Zhengzhou Henan China California State University Department of Computer Science SacramentoCA95819-6021 United States Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States

ISBN: (纸本)9781728190747

Temperature is one of the major ecological factors that affect the safe storage of grain. In this paper, we propose a deep spatiooral attention mode to predict stored grain temperature, which exploits the historical temperature data of stored grain and the meteorological data of the region. In this proposed model, we use the Sobel operator to extract the local spatial factors, and leverage the attention mechanism to obtain the global spatial factors of grain temperature data and temporal information. In addition, a convolutional neural network (CNN) is used to learn features of external meteorological factors. Finally, the spatial factors of grain pile and external meteorological factors are combined to predict future grain temperature using long short-term memory (LSTM) based encoder and decoder models. Experiment results show that the proposed model achieves higher predication accuracy compared with the traditional methods. © 2020 IEEE.

关键词： Piles

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

GA-OMTL: Genetic algorithm optimization for multi-task neural architecture search in NIR spectroscopy

引用

Expert Systems with Applications 2025年 290卷

作者： Yu Yang Siqi Wang Gan Zhang Qifu Wang Yao Qin Dandan Zhai Zhiqing Yang Peng Li Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou 450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou 450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Institute for Complexity Science Henan University of Technology Zhengzhou 450001 China Henan Academy of Science Applied Physics Institute Co. Ltd Zhengzhou 450001 China

Near-infrared (NIR) spectroscopy analysis based on deep learning has emerged as a powerful tool for the quality assessment of food and agricultural products. However, most existing multi-task deep learning (MTL) network architectures rely on manual design, struggle to efficiently adapt to diverse datasets, and often neglect the interaction of task-specific (private) features across tasks. To address these challenges, this study proposes a genetic algorithm (GA)-optimized MTL model, termed GA-OMTL, which integrates the strengths of neural architecture search and GA for multi-task prediction of spectral data. The model enhances both feature extraction and task-specific feature interaction by incorporating searchable components such as residual modules (Resblock), batch normalization (BN) layers, Squeeze-and-Excitation (SE) modules, and feature interaction modules. The effectiveness of GA-OMTL was validated using two datasets: American ginseng and wheat flour. In the prediction of protopanaxatriol-type ginsenosides (PPT) and protopanaxadiol-type ginsenosides (PPD) in American ginseng, the R 2 , RMSE , and RPD values achieved by GA-OMTL were 0.93, 0.70 mg/g, and 3.83 (PPT), and 0.98, 2.03 mg/g, and 7.16 (PPD), respectively. For the prediction of protein and moisture content in wheat flour, the R 2 , RMSE , and RPD values were 0.99, 0.29 mg/g, and 8.22 (protein), and 0.97, 0.22 mg/g, and 5.67 (moisture), respectively. The experimental results demonstrate that GA-OMTL outperforms three comparison methods in prediction accuracy, highlighting its potential for complex spectral tasks and confirming the practicality and robustness of the proposed model.

关键词：

来源：评论

学校读者我要写书评

暂无评论

NIRDiffusion: A diffusion-model-based framework for enhanced quality assessment of industrial plant materials

引用

Industrial Crops and Products 2025年 232卷

作者： Yu Yang Siqi Wang Jinlong Duan Wei Zhang Qifu Wang Dandan Zhai Yao Qin Zhiqing Yang Peng Li Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou 450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou 450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Institute for Complexity Science Henan University of Technology Zhengzhou 450001 China Henan Academy of Science Applied Physics Institute Co. Ltd Zhengzhou 450001 China

Soybean and American ginseng, as industrial plant-derived materials, require precise quantification of soluble proteins and ginsenosides due to their importance in industrial applications such as bio-based materials and pharmaceutical ingredient production. Near-infrared (NIR) spectroscopy, combined with advanced AI models, offers a rapid, non-invasive, and cost-effective approach for evaluating the quality of industrial plant-derived materials. However, its application is limited by challenges such as high spectral dimensionality, environmental noise, and small labeled datasets. To address these issues, this study proposes a diffusion-model-based representation learning framework, NIRDiffusion, which utilizes a probabilistic, iterative process of perturbation and reversal to capture multi-scale non-linear spectral latent features while effectively suppressing noise. The proposed NIRDiffusion combined with a 1D-CNN regression model achieves R 2 values of 0.973 and 0.949 in predicting water-soluble protein (WSP) in soybean seeds and total ginsenosides (TG) in American ginseng, respectively. Comparative analyses reveal significant prediction error reductions of at least 17.89 % (WSP) and 11.21 % (TG) relative to popular unsupervised feature extraction and three self-supervised learning approaches. These results demonstrate that NIRDiffusion effectively captures discriminative features in NIR spectral data, enabling accurate and scalable quality assessment of plant-derived materials through integration with predictive modeling.

关键词： Near-infrared spectroscopy Diffusion model Generative representation learning Industrial materials

来源：评论

学校读者我要写书评

暂无评论

An Anomaly Detection Model for ADS-B Systems Based on Improved GAN and LSTM Networks

An Anomaly Detection Model for ADS-B Systems Based on Improv...

引用

International Conference on Communication technology (ICCT)

作者： Xinying Guo Chunhua Zhu Jing Yang Yan Xiao Key Laboratory of Grain Information Processing and Control Henan University of Technology Zhengzhou China Ministry of Education College of Information Science and Engineering Henan University of Technology Zhengzhou China Zhengzhou Locaris Technology Co. LTD Zhengzhou China

ISBN: (纸本)9781665432078

Automatic Dependent Surveillance-Broadcast (ADS-B) is an important part of the next generation air transportation systems, but its protocol does not provide relevant authentication and data encryption, so it is extremely vulnerable to various spoofing attacks. Therefore, it is necessary to develop a set of ADS-B data anomaly detection models. According to the strong temporal dependence and fast update rate of ADS-B data, we initiatively introduce a novel anomaly detection model based on Improved Generative Adversarial Network and long short-term memory networks (IGAN-LSTM). In the model, IGAN mainly improves the generator $G$ architecture of GAN through upgrading the commonly used encoder architecture to encoder-decoder-encoder architecture, which captures the training data distribution within both data space and latent space. IGAN then utilizes encoding losses based on latent spatial features to determine whether a data sample is anomalous or not. At the same time, to model ADS-B data with the temporal dependence, we use the LSTM networks as the main architecture of the model, which is able to make use of long-term temporal dependence and avoid the vanishing gradient problem during training. Experimental results show that the proposed model has a higher area under the receiver operating characteristic curve and the precision recall curve than the other baseline methods from the literature, and reveal that the model has better performance of anomaly detection of ADS-B data.

关键词： Training Training data Receivers Generative adversarial networks Feature extraction Data models Generators

来源：评论

学校读者我要写书评

暂无评论

A Deep Reinforcement Learning-based Contention Window Avoidance Scheme for Distinguishing Network Service Priorities

A Deep Reinforcement Learning-based Contention Window Avoida...

引用

International Conference on Computing, Networking and Communications (ICNC)

作者： Xiaowei Nie Demin Wang Yuqing Zhang Xiaoduo Pan Zhibin Zuo National Tibetan Plateau Data Center (TPDC) State Key Laboratory of Tibetan Plateau Earth System Environment and Resources (TPESER) Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing China Science Center of Lingshan Forum of Guangdong Province Guangzhou China School of Ecology and Environment Tibet University Lhasa China College of Information Science and Engineering Henan University of Technology Zhengzhou China Henan International Joint Laboratory of Grain Information Processing Zhengzhou China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

This study introduces a Deep Reinforcement Learning-based Contention Window Avoidance Scheme aimed at improving service priority differentiation and network throughput under high load conditions within wireless networks that utilize the Network Distributed Coordination Function (DCF) mechanism. This novel approach involves two key strategies: firstly, it enables the differentiation of service priorities within DCF-based channel access by categorizing them in accordance with the varying data types and streams transmitted by different stations; secondly, it leverages Deep Reinforcement Learning to dynamically evaluate the network state through analysis of collision probabilities derived from channel observations. By doing so, the scheme dynamically modulates the ranges of contention windows in alignment with the service priorities, responding to the immediate network circumstances. Empirical evidence indicates that the proposed scheme substantially mitigates collision frequencies and augments data transmission throughput, thus significantly advancing the overall performance of networks that require service priority differentiation.

关键词： Frequency modulation Wireless networks Throughput Deep reinforcement learning Data communication Streams Optimization

来源：评论

学校读者我要写书评

暂无评论

An Improvement of Data Cleaning Method for grain Big Data processing Using Task Merging

引用

Journal of Computer and Communications 2020年第3期8卷 1-19页

作者： Feiyu Lian Maixia Fu Xingang Ju Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Ministry of Education Zhengzhou China College of Information Science and Technology Henan University of Technology Zhengzhou China.

Data quality has exerted important influence over the application of grain big data, so data cleaning is a necessary and important work. In MapReduce frame, parallel technique is often used to execute data cleaning in high scalability mode, but due to the lack of effective design, there are amounts of computing redundancy in the process of data cleaning, which results in lower performance. In this research, we found that some tasks often are carried out multiple times on same input files, or require same operation results in the process of data cleaning. For this problem, we proposed a new optimization technique that is based on task merge. By merging simple or redundancy computations on same input files, the number of the loop computation in MapReduce can be reduced greatly. The experiment shows, by this means, the overall system runtime is significantly reduced, which proves that the process of data cleaning is optimized. In this paper, we optimized several modules of data cleaning such as entity identification, inconsistent data restoration, and missing value filling. Experimental results show that the proposed method in this paper can increase efficiency for grain big data cleaning.

关键词： grain Big Data Data Cleaning Task Merging Hadoop MapReduce

来源：评论

学校读者我要写书评

暂无评论

Variational quantum process tomography of unitaries

引用

Physical Review A 2022年第3期105卷 032427-032427页

作者： Shichuan Xue Yong Liu Yang Wang Pingyu Zhu Chu Guo Junjie Wu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China College of Information and Communication National University of Defense Technology Xi'an 710006 China Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou Henan 450000 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China

Quantum process tomography is an experimental technique to fully characterize an unknown quantum process. Standard quantum process tomography suffers from exponentially scaling of the number of measurements with the increasing system size. In this work, we put forward a quantum machine learning algorithm which approximately encodes the unknown unitary quantum process into a relatively shallow depth parametric quantum circuit. We demonstrate our method by reconstructing the unitary quantum processes resulting from the quantum Hamiltonian evolution and random quantum circuits up to eight qubits. Results show that those quantum processes could be reconstructed with high fidelity, while the number of input states required are at least two orders of magnitude less than required by the standard quantum process tomography.

关键词： Machine learning Quantum information processing Quantum simulation Quantum tomography

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：