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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Zuo, Xianyu Yuan, Bo Li, Shilong Dang, Lanxue Lin, Yinghao Han, Hongyu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781510664616

With the development of network technology and people's growing need for a better life, IoT technology has been integrated into more and more people's daily lives. At the same time, due to the different needs of individual users and the differences in the function of networking equipment spawned a variety of Internet of things products. Hence an IoT platform that is convenient, efficient and easy to expand is particularly important. This article designs and implements an integrated IoT platform for many types of persistent connected IoT devices on the market. The platform supports concurrent access of large-scale IoT devices with multiple sources and protocols, and supports remote management of devices through instructions. Monitor the health of the equipment in real time and alarm in real time in case of abnormalities. © 2023 SPIE.

关键词： Internet of things

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Intelligent Service Cluster Construction Method Based on Remote Sensing Application 6

Intelligent Service Cluster Construction Method Based on Rem...

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6th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2022

作者： Zhou, Liming Yang, Wencheng Du, Ying Liu, Cheng Li, Heng Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781665431859

The production of remote sensing products needs to go through various links of evaluation and deploy a variety of software and hardware resources. It is necessary to study how to improve the efficiency of service cluster deployment, simplify the evaluation process, and optimize its structure. This paper designs an intelligent service cluster construction method based on remote sensing applications, which realizes the accurate and efficient deployment of various complex network structures and equipment on the visual interface. The method is convenient and simple to operate. It can set various types of resource information according to the needs of users, can choose a more reasonable server cluster deployment plan, improve the efficiency of data storage and processing of remote sensing products, better serve users, and greatly promote The development of related industries. © 2022 IEEE.

关键词： Remote sensing

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Integrated multi-dimensional communication system for dam and bank risk situations on the Yellow River

Integrated multi-dimensional communication system for dam an...

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Dang, Lanxue Zhang, Miaoxin Xie, Yi Zuo, Xianyu Zhang, Lei Fan, Minghu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781510664616

Due to the terrain, the frequent floods of the Yellow River have posed a great threat to the cities along the Yellow River. At present, although the danger alarm system and video surveillance system are widely installed along the Yellow River Basin, there is a lack of linkage between the two, which wastes monitoring resources. In addition, the push rules of different types of alarm signals are relatively complex, and the coupling between rules and system implementation is high, which is not conducive to rule modification and expansion;moreover, there are many cities along the Yellow River, if each city customizes its own system, it will lead to more overhead. Therefore, this problem can be solved by improving the hierarchical scalability of the system. So, based on the Drools framework, this paper uses the rule engine to realize the unified processing of different rules and the linkage integration of sensors and cameras, and designs a recursive model to simplify the development process, so as to realize the accurate positioning of alarm information at all levels of provinces, cities and counties, and the hierarchical processing of various alarm information. © 2023 SPIE.

关键词： Alarm systems

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key-Point Based Distribution Probability Optimization for Object Detection in Remote Sensing Images 7

Key-Point Based Distribution Probability Optimization for Ob...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Zhu, Xiaoke Wang, Yufei Chen, Xiaopan Li, Danyang Zhang, Jitong Cai, Yulong Henan University School of Computer and Information Engineering Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781665409506

Recently, object detection in remote sensing images has been an important topic in computer vision. As a mainstream approach to detectors, single-stage detectors usually compute the important points of the bounding box to get the location of the target, and optimize them by Focal Loss. One of the development trends for one-stage detectors is to combine with Localization Quality Estimation (LQE), which can provide accurate ranking scores, and thus bring benefits to the Non-Maximum Suppression as well as the following detection results. In this paper, we propose a one-stage detector, named key-point based Distribution Probability Optimization (KDPO), for remote sensing object detection. Specially, we first classify and locate the object through five key points. Then, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification. Next, we utilize a vector to represent arbitrary distribution of box locations. The improved representations avoid the inconsistency risk and accurately describe the flexible distribution of the data. Meanwhile, we use the improved Focal Loss for the succeed optimization. The proposed method is tested on two popular aerial image datasets, including HRSC2016 and DOTA. In all experiments, our approach achieves promising performance comparing to the state-of-the-art detectors. © 2021 IEEE.

关键词： Object detection

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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： 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

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.

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Solving multi depot school bus routing problem based on simulated annealing algorithm

Solving multi depot school bus routing problem based on simu...

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2022 International Conference on Algorithms, Microchips and Network Applications

作者： Han, Daojun Xiang, Hongzhen Xie, Yi Key Laboratory of Big Data Analysis and Processing of Henan Province Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781510653290

Multi depot school bus routing problem (MDSBRP) refers to the school bus routing problem (SBRP) with multiple depots, which effectively reduces the service cost by adjusting the order of school buses from depot to station. In this paper, a heuristic algorithm based on simulated annealing is proposed to solve the multi depot school bus routing problem. The problem considers the constraints of school time window and school bus capacity. Through the operation adjustment of depots and stations, the number and driving distance of school buses can be effectively reduced. The existing benchmark case set and modified test cases are used to test the algorithm. The experimental results show that the use of multi depot mode can effectively reduce the number of school buses and save operating costs, which shows the effectiveness of the algorithm. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Heuristic algorithms

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Adaptive Method of Process Flow Parameter Values Setting Based on Remote Sensing Image Metadata

Adaptive Method of Process Flow Parameter Values Setting Bas...

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Zhou, Bing Ge, Jian Kang Zuo, Xianyu Key Laboratory of Analysis and Processing of Big Data Henan Kaifeng475004 China School of Computer and Information Engineering Henan University Henan Kaifeng475004 China

ISBN: (纸本)9781510664616

Remote sensing data processing involves many steps. This paper uses process flow to represent these processing steps. This paper combines remote sensing image metadata (RSIM) with case based reasoning (CBR) technology, proposes a parameter value adaptive method based on RSIM, and constructs a remote RSIM case model. It makes the representation of RSIM more intuitive and convenient. This method consists of two parts: case building and case similarity measurement. Case building refers to building RSIM into RSIM cases according to case models, and then classifying and saving each case. Case similarity measurement refers to the similarity measurement of metadata in the production process and each case under the flow. Finally, the parameter values will be configured according to the highest similarity. The method is tested in an actual remote sensing production system, and the experimental results show that the method has good applicability in the parameter value adaptation of remote sensing process production. © 2023 SPIE.

关键词： Remote sensing

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A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment analysis 24

A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment An...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Dang, Lanxue Wang, Chunyu Han, Hongyu Hou, Yan-E Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China School of Computer Science and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9798350319934

Sentiment analysis is an important and challenging task in the field of natural language processing. Researchers have used vocabulary-based methods and machine learning methods to conduct research on sentiment analysis tasks. In recent years, deep learning has achieved great success in the field of sentiment analysis. However, for complex textual data, using a single model is often insufficient. Inspired by deep learning models, in this study, we proposed a hybrid model using BiLSTM and attention mechanism, called BiLSTM-ATT model, to solve the problem of sentiment analysis. First, we adopted the GloVe method to train the initialized word embeddings. GloVe converts textual information into word vectors, which can calculate the distance between words. Next, we used a convolutional neural network that can extract local features, and a BiLSTM that can extract long-range semantic features of text with bidirectional extraction of long-term dependencies. Finally, the attention mechanism was used to improve the performance of the model by calculating the weight of the data. Experimental results demonstrate that our proposed hybrid BiLSTM-ATT model outperforms traditional deep learning methods in Accuracy, Recall, and Fl-Score. Our method was compared with deep learning methods on the IMDB movie review dataset. © 2022 IEEE.

关键词： Sentiment analysis

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Multiple granularity network and dynamic label for domain adaptive person re-identification 4

Multiple granularity network and dynamic label for domain ad...

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2021 4th International Conference on Computer Information Science and Application Technology, CISAT 2021

作者： Wang, Xile Zhang, Sihan Song, Junyu Zhang, Miaohui Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Kaifeng475004 China School of Artificial Intelligence Henan University Henan Kaifeng475004 China

The domain adaptive person re-identification (Re-ID) has be more popular among researchers. Because it can save a lot of resources as it only exploits the source domain knowledge and does not need the complex annotation efforts in target domain. It aims to extend a model trained on a labeled dataset to another dataset which is unlabeled. Many works reduce feature distribution gap between two different datasets to solve the problem. However, these works ignore the problem which is the variations within an unlabeled dataset. In the paper, we propose a domain adaptive person Re-ID framework based on multiple granularity network and dynamic label (MGDL). Specifically, we send the images of two different datasets into multiple granularity network at the same time for joint training to reduce feature distribution gap which is between the two different datasets. The network is trained by two different kinds of pseudo labels, namely, conservative label and radical label. The two kinds of pseudo labels are used to alternating pull and push the feature distribution in the target domain to reduce the variations within an unlabeled dataset. Experiments have shown that the MGDL achieves competitive performance in person Re-ID which is under the cross-domain setting. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

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Depth Guided Cross-modal Residual Adaptive Network for RGB-D Salient Object Detection 2

Depth Guided Cross-modal Residual Adaptive Network for RGB-D...

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2021 2nd International Workshop on Electronic communication and Artificial Intelligence, IWECAI 2021

作者： Zhao, Zhengyun Yang, Qingpeng Yang, Shangqin Wang, Jun School of Artificial Intelligence Henan University Kaifeng Henan475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng Henan475004 China

Depth modal features can provide complementary information for salient object detection (SOD). Most of the existing RGB-D SOD methods focus on fully combining RGB and Depth modal features without distinguishing them. In this paper, we propose a new depth guided cross-modal residual adaptive network for RGB-D SOD. We use two independent resnet-50 to extract the features of the two modes respectively. Then the cross-modal channel-wise refinement module is designed to obtain complementary modal information. We design a crossmodal guided module to make complementary modal information guide RGB image feature extraction. Finally, the residual adaptive selection module is used to enhance the spatial mutual concerns between the two modal features to achieve multimodal information fusion. Experimental results show that our method can achieve a more reasonable fusion state of RGB and Depth, and verify the effectiveness of our final saliency model. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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