检索结果-内蒙古大学图书馆

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud

arXiv

引用

arXiv 2024年

作者： Chen, Yirui Wei, Pengjin Liu, Zhenhuan Wang, Bingchao Yang, Jie Liu, Wei Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education China

Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively. Our code is publicly available at https://***/chenyirui/FASTC. © 2024, CC BY.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Relation-Aware Facial Expression Recognition Using Contextual Residual Network with Attention Mechanism 17th

Relation-Aware Facial Expression Recognition Using Contextua...

引用

17th International Conference on Computer Science and Education, ICCSE 2022

作者： Li, Xue Zhu, Chunhua Zhou, Fei Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Henan Zhengzhou China College of Information Science and Engineering Henan University of Technology Henan Zhengzhou China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou China

ISBN: (纸本)9789819924424

With the existence of occlusion or posture changes, facial expression recognition (FER) under uncontrolled conditions is difficult. To obtain a discriminative representation for challenging expression recognition, we present a relation-aware FER model, referred to attention-coordinated contextual residual network (ACRN), which contains a contextual residual network (CRNet) and attention module (AM). Firstly, multi-level features of facial expression are extracted by CRNet. There are some contextual convolution (CoConv) blocks in CRNet to integrate expression information in facial space;then, AM is embedded into each stage of CRNet to coordinate contextual information from CoConv blocks across the channel and spatial location, weighting multi-level expression features to differentiate their importance;finally, to highlight expression-related facial areas, the output of CRNet will be fed into AM, which can focus on salient features. The experiments on Affectnet-7 and RAF_DB datasets have shown that ACRN can both explore the interaction of facial information and capture subtle features related to expression, thus obtaining a better recognition performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Robust Optimization for Energy Chemical Processes under Uncertainties: A Review and Tutorial 4

Data-Driven Robust Optimization for Energy Chemical Processe...

引用

4th International Conference on Industrial Artificial Intelligence, IAI 2022

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

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In recent years, data-driven robust optimization (DDRO) is becoming a popular and effective paradigm to address the challenging issue of uncertainty in energy chemical processes. This paper provides an overview of recent advances in the field of DDRO, with a primary focus on its methods and applications in process industries. Firstly, a brief introduction to various robust optimization model formulations and solution algorithms is presented. Secondly, research achievements of machine-learning enabled uncertainty sets, the corresponding DDRO, and variant techniques are summarized and analyzed in a systematic manner. Additionally, tutorial-like numerical examples are used to illustrate merits of DDRO compared with conventional robust optimization. Finally, fruitful applications of DDRO in energy chemical processes are encapsulated and categorized from domain perspectives. © 2022 IEEE.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnosis Method Based on CND-SMOTE and BA-SVM Algorithm 2

Fault Diagnosis Method Based on CND-SMOTE and BA-SVM Algorit...

引用

2022 2nd International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2022

作者： Wang, Sheng Ma, Liling Wang, Junzheng Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing100081 China

The problem of unbalanced data classification has gotten extensive attention in the past few years. Unbalanced sample data makes the fault diagnosis and classification accuracy rate low, and the capability to classify minority-class fault samples is restricted. To address the problem that the classification algorithm in machine learning has the insufficient capability to identify minority class samples for unbalanced sample data classification problems. Therefore, this paper proposes an improved support vector machine (SVM) classification method based on the synthetic minority over-sampling technique (SMOTE). For the sampler, an improved synthetic minority over-sampling technique based on the characteristics of neighborhood distribution (CND-SMOTE) algorithm is used to equilibrate the minority class samples and the majority class samples. For the classifier, the parameter optimization method of support vector machines based on the bat algorithm (BA-SVM) is used to solve the multi-classification problem of faulty samples. Finally, experimental results prove that the CND-SMOTE+BA-SVM algorithm can synthesize high-quality minority fault samples, increase the classification accuracy rate of fault samples, and decrease the time spent on the classification. © Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Event-Triggered Online Learning Distributionally Robust Energy Management of Ammonia-Based Multi-Energy Microgrid 4

Event-Triggered Online Learning Distributionally Robust Ener...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Efficient dynamic fusion for multispectral pedestrian detection 12

Efficient dynamic fusion for multispectral pedestrian detect...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

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：