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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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Design and implementation of highly scalable IoT platform based on Netty

Design and implementation of highly scalable IoT platform ba...

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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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Research on Workflow Model and Verification Method of Remote Sensing data processing 17th

Research on Workflow Model and Verification Method of Remote...

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

作者： Qiao, Baojun He, Siyuan Du, Ying Zuo, Xianyu 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)9789819924424

To reduce the uncertainty factors in the remote sensing data processing process and the complexity of remote sensing data processing software, a remote sensing data processing workflow model is presented based on Petri net and UniNet theory. The model can not only describe the data, tasks and their logical relationships involved in remote sensing data processing workflow formally, but also analyze the logic of the process dynamically. Furthermore, the model can be effectively combined with workflow technology, which provide great help for solving remote sensing application needs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Petri nets

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Winter wheat phenology classification using stacking ensemble learning algorithm based on Sentinel-1A SAR images 5

Winter wheat phenology classification using stacking ensembl...

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5th International Conference on Geoscience and Remote Sensing Mapping, ICGRSM 2023

作者： Duan, Haoxin Yang, Huijin Zhao, Jianhui Li, Ning School of Computer and Information Engineering Henan University Kaifeng China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781510672789

Crop monitoring and phenology estimation based on the satellite systems have become an important research area due to high demand on crops. Synthetic Aperture Radar (SAR) is a kind of microwave remote sensing equipment, which has the advantage of all-weather and all-day, and can realize large-scale and periodic crop phenological monitoring. Besides, thanks to the high temporal resolution of new generation space-based sensors, it has been possible to monitor growth cycle of crops by classification algorithms. A stacking ensemble learning algorithm using time series Sentinel-1A SAR images for winter wheat phenology classification was proposed in this paper based on multiple machine learning models, including Random Forest (RF), Support Vector Machine (SVM), K-nearest Neighbor(K-NN), Naive Bayes (NB) and BP Neural Network (BP) models. The experimental results showed that, comparing with each single model, the stacking ensemble learning algorithm proposed in this paper had the optimal performance, with the highest overall recognition accuracy of 81.40%, demonstrating its effectiveness and application potential for winter wheat phenology identification. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Synthetic aperture radar

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Learning on Less: Constraining Pre-trained Model Learning for Generalizable Diffusion-Generated Image Detection

arXiv

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arXiv 2024年

作者： Chen, Yingjian Zhang, Lei Niu, Yakun Tan, Lei Chen, Pei Henan Key Laboratory of Big Data Analysis and Processing Henan University China

Diffusion Models enable realistic image generation, raising the risk of misinformation and eroding public trust. Currently, detecting images generated by unseen diffusion models remains challenging due to the limited generalization capabilities of existing methods. To address this issue, we rethink the effectiveness of pre-trained models trained on large-scale, real-world images. Our findings indicate that: 1) Pre-trained models can cluster the features of real images effectively. 2) Models with pre-trained weights can approximate an optimal generalization solution at a specific training step, but it is extremely unstable. Based on these facts, we propose a simple yet effective training method called Learning on Less (LoL). LoL utilizes a random masking mechanism to constrain the model’s learning of the unique patterns specific to a certain type of diffusion model, allowing it to focus on less image content. This leverages the inherent strengths of pre-trained weights while enabling a more stable approach to optimal generalization, which results in the extraction of a universal feature that differentiates various diffusion-generated images from real images. Extensive experiments on the GenImage benchmark demonstrate the remarkable generalization capability of our proposed LoL. With just 1% training data, LoL significantly outperforms the current state-of-the-art, achieving a 13.6% improvement in average ACC across images generated by eight different models. Copyright © 2024, The Authors. All rights reserved.

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Research on resource scheduling method based on Kubernetes 8

Research on resource scheduling method based on Kubernetes

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8th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2025

作者： Song, Haoyang Zhou, Liming Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9798331517915

With the continuous development of cloud computing, the demand for cloud services in all walks of life is increasing. The scale of cloud computing services based on container technology is constantly growing, and Kubernetes has been widely used as a mainstream tool for container orchestration. It supports functions such as automatic deployment and service discovery, dramatically simplifies the container deployment process, and improves the availability and reliability of applications in cloud environments. However, the default scheduling strategy of Kubernetes adopts the greedy idea of first-come-first-served when facing multi-pod tasks, and schedules Pods to working nodes one by one. This scheduling scheme will lead to unbalanced cluster load and low resource utilization. To solve this problem, this paper introduces the selection, crossover, and mutation ideas in genetic algorithms into the artificial fish school algorithm, improves the foraging behavior in the artificial fish school algorithm, and constructs a custom scheduling strategy based on the enhanced artificial fish school algorithm. Experiments have shown that compared with the default scheduling strategy of Kubernetes, the customized scheduling strategy in this paper can effectively improve the load balancing of the cluster and improve the utilization of cluster hardware resources in the scenario of multi-pod scheduling. © 2025 IEEE.

关键词： container technology containerd improved artificial fish swarm algorithm Kubernetes

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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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CLIP-HASH: A Lightweight Hashing Network for Cross-Modal Retrieval 26

CLIP-HASH: A Lightweight Hashing Network for Cross-Modal Ret...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Mengying Lai, Hanjiang Yin, Jian Xu, Mengying Sun Yat-Sen University School of Systems Science and Engineering Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9798350331684

Cross-modal hashing has attracted great interest in the past decades. Due to the traditional hashing retrieval model requiring hand-crafted feature extraction, much research on the end-to-end deep hashing models has been explored. However, this training process of deep models is complex and needs a large number of samples. To address the above issues, we propose a lightweight hashing retrieval network with the pre-trained CLIP model, which is called CLIP-Hash. It can obtain better hash features via the pre-training CLIP model and provide a lightweight fine-tune model which is easily trained and only few training samples are required. First, we obtain the powerful feature representations by the visual and textual encodes. Then, we propose a simple hash encoding module, which is a lightweight network and only few parameters are needed to be trained. With the proposed hash encoding module, we can obtain efficient binary hash codes that can align the images and texts. We conduct experiments on MIRFlickr and NUS-WIDE two benchmark datasets. The results show that the proposed simple method can outperform the SOTA hashing methods. © 2023 IEEE.

关键词： Encoding (symbols)

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A Review of Detection-Related Multiple Object Tracking in Recent Times 26

A Review of Detection-Related Multiple Object Tracking in Re...

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Li, Suya Cao, Ying Xie, Xin Henan University School of Computer and Information Engineering Jinming Campus Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Jinming Campus Kaifeng China

ISBN: (纸本)9791188428120

Multi-object tracking (MOT) is garnering more attention due to its widespread application in the area of autonomous driving, human-computer interaction, and intelligent video surveillance. Especially in recent years, MOT has rapidly developed thanks to related technologies such as object detection, which has helped in handling interfering factors such as crowded scene occlusion, small objects, and similar appearances. Among these, Detection-based MOT is the mainstream for accurately forming objects' trajectories. Therefore, according to the analysis of the last three years' research, this paper particularly focuses on discussing the continuous optimization strategies of MOT around the development of object detection at each stage. In addition, this article also introduces the commonly used benchmark datasets and related applications of MOT. © 2024 Global IT Research Institute - GIRI.

关键词： Object detection

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Attention based simplified deep residual network for citywide crowd flows prediction

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Frontiers of Computer Science 2021年第2期15卷 51-62页

作者： Genan DAI Xiaoyang HU Youming GE Zhiqing NING Yubao LIU School of Data and Computer Science Sun Yat-Sen UniversityGuangzhou510006China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006China

Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the *** practice,emergency applications often require less training ***,there is a little work on how to obtain good prediction performance with less training *** this paper,we propose a simplified deep residual network for our *** using the simplified deep residual network,we can obtain not only less training time but also competitive prediction performance compared with the existing similar ***,we adopt the spatio-temporal attention mechanism to further improve the simplified deep residual network with reasonable additional time *** on the real datasets,we construct a series of experiments compared with the existing *** experimental results confirm the efficiency of our proposed methods.

关键词： crowd flows prediction spatio-temporal data mining attention

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