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12th International Conference on Information Science and Technology, ICIST 2022

作者： Liu, Yang Wang, Ruiyi Cao, Kejing Wang, Jiuhao Shi, Zezhao Wang, Yadi Zhou, Yi Shenzhen Research Institute Henan University Henan Province Engineering Research Center of Spatial Information Processing College of Computer and Information Engineering Kaifeng China College of Computer and Information Engineering Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China College of Computer and Information Engineering Henan University Henan Province Engineering Research Center of Spatial Information Processing Kaifeng China College of Artificial Intelligence Henan University Shenzhen Research Institute Henan University Zhengzhou China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

Due to the complexity and diversity of underwater environment, high-precision and fast target detection is a scientific problem in underwater acoustic information extraction, especially the underwater target detection of sonar images still has a technical bottleneck. With the development of intelligent detection technology, as the state of the art model, target detection model based on deep neural network adopts different scale feature extraction mechanism, which is easy to generate false alarm for important targets and difficult to overcome the contradiction between false detection and missed detection. The attention mechanism can fully learn the features of the target and improve the accuracy of target detection. Considering the characteristics of seabed exploration task and underwater target, we propose a deep convolution network based on dual channel attention mechanism (DCNet), This model can strengthen the features of the target of interest while weakening the irrelevant background noise information, so as to improve the detection accuracy of the target and enhance the detection ability of the underwater target. The experimental results show that the average accuracy of the dual channel attention mechanism can achieve higher accuracy than the original model, and is superior to other target detection models in accuracy and performance. This research has important practical significance for improving the task of underwater target detection of sonar images and has a wide range of engineering application prospects in the detection of underwater acoustic systems. © 2022 IEEE.

关键词： Underwater acoustics

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TMFNet: Two-Stream Multi-Channels Fusion Networks for Color Image Operation Chain Detection

arXiv

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arXiv 2024年

作者： Niu, Yakun Tan, Lei Zhang, Lei Zuo, Xianyu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475000 China School of Computer and Information Engineering Henan University Kaifeng475000 China

Image operation chain detection techniques have gained increasing attention recently in the field of multimedia forensics. However, existing detection methods suffer from the generalization problem. Moreover, the channel correlation of color images that provides additional forensic evidence is often ignored. To solve these issues, in this article, we propose a novel two-stream multi-channels fusion networks for color image operation chain detection in which the spatial artifact stream and the noise residual stream are explored in a complementary manner. Specifically, we first propose a novel deep residual architecture without pooling in the spatial artifact stream for learning the global features representation of multi-channel correlation. Then, a set of filters is designed to aggregate the correlation information of multi-channels while capturing the low-level features in the noise residual stream. Subsequently, the high-level features are extracted by the deep residual model. Finally, features from the two streams are fed into a fusion module, to effectively learn richer discriminative representations of the operation chain. Extensive experiments show that the proposed method achieves state-of-the-art generalization ability while maintaining robustness to JPEG compression. The source code used in these experiments will be released at https://***/LeiTan98/TMFNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Image fusion

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Lightweight Fine-Tuning a Pretrained Protein Language Model for Protein Secondary Structure Prediction

SSRN

引用

SSRN 2023年

作者： Yang, Wei Liu, Chun Li, Zheng Henan Key Laboratory of Big Data Analysis and Processing Henan Engineering Laboratory of Spatial Information Processing School of Computer and Information Engineering Henan University Kaifeng475004 China

Pretrained large-scale protein language models, such as ESM-1b and ProtTrans, are becoming the fundamental infrastructure for various protein-related biological modeling tasks. Existing works use mainly pretrained protein language models in feature extraction. However, the knowledge contained in the embedding features directly extracted from a pretrained model is task-agnostic. To obtain task-specific feature representations, a reasonable approach is to fine-tune a pretrained model based on labeled datasets from downstream tasks. To this end, we investigate the fine-tuning of a given pretrained protein language model for protein secondary structure prediction tasks. Specifically, we propose a novel end-to-end protein secondary structure prediction framework involving the lightweight fine-tuning of a pretrained model. The framework first introduces a few new parameters for each transformer block in the pretrained model, then updates only the newly introduced parameters, and then keeps the original pretrained parameters fixed during training. Extensive experiments on seven test sets, namely, CASP12, CASP13, CASP14, CB433, CB634, TEST2016, and TEST2018, show that the proposed framework outperforms existing predictors and achieves new state-of-the-art prediction performance. Furthermore, we also experimentally demonstrate that lightweight fine-tuning significantly outperforms full model fine-tuning and feature extraction in enabling models to predict secondary structures. Further analysis indicates that only a few top transformer blocks need to introduce new parameters, while skipping many lower transformer blocks has little impact on the prediction accuracy of secondary structures. Our code and datasets are publicly available at https://***/fengtuan/LIFT_SS. © 2023, The Authors. All rights reserved.

关键词： Proteins

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3d-Usanet：A Spatio-Temporal Coupled Based Transformer for Video Anomaly Detection Method

SSRN

引用

SSRN 2024年

作者： Mu, Huiyu Wang, Luhui Yin, Hongjian Li, Yonggan Dang, Lanxue Liu, Yang Zuo, Xianyu School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Fair Competition Review Transaction Center of Henan Province Zhengzhou450008 China

Video anomaly detection aims to detect incidental and sudden patterns of events in complex scenes, which plays an essential role for maintaining public security and averting potential risks. Unsupervised learning is particularly effective in solving the problem of insufficient annotation of video abnormal behaviour in low resource scenarios. However，most relate methods decompose it into two sub-tasks: spatio anomaly detection (S-VAD) and temporal anomaly detection (T-VAD), which barely consider the coupled correlations and the difficulty of coupling global spatio-temporal information. To address this, we provide a new perspective on constructing a novel dynamic spatio-temporal coupling relationship by designing a 3D U-shape Attention Network (3D-UsANet). On the one hand, we construct a Spatio-Temporal Feature Modelling Transformer (STFMT) module to handle different spatio positions at the same time step. On the other hand, a Channel-wise Scale-aware Fusion Modelling Transformer (CSFMT) module is integrated into 3D-UsANet to overcome the multi-scale heterogeneous divides and disregard irrelevant characteristics, which adaptively captures global semantic dependencies and bridges the spatio-temporal semantic gap. Essentially, we evaluate the performance of 3D-UsANet on UCSD Ped1、UCSD Ped2 and CUHK Avenue datasets with the AUC score 83.50%、96.30%、86.70%，respectively. The extensive experimental results demonstrate the effectiveness of 3D-UsANet in detecting anomaly behaviors. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Research progress on an intelligent production and distribution of the remote sensing products

Research progress on an intelligent production and distribut...

引用

International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2020

作者： Kong, Dezhen Zhao, Jinhuan Tian, Meng Liu, Yang Henan Province Space Information Processing Engineering Laboratory KaifengHenan475004 China Henan Key Laboratory of Big Data Analysis and Processing KaifengHenan475004 China School of Computer and Information Engineering Henan University KaifengHenan475004 China

ISBN: (纸本)9783030539795

Since the amount of remote sensing data that can be obtained every day has reached ten terabytes, the storage and sharing of massive remote sensing data and the construction of product production application architecture have become imminent research topics. The purpose of this study is to build an efficient system that can meet the needs of massive remote sensing data storage, calculation, and distribution. The article elaborates on the latest research progress of cloud service remote sensing data distribution and intelligent production from the four aspects of remote sensing cloud service development, data distribution technology, cloud service technology, and operation mechanism of remote sensing cloud computing processing model, and proposes cloud service remote sensing key issues facing data distribution, and summarize and prospect related research, with a view to promoting the further development of research on cloud service remote sensing data distribution and intelligent production. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Remote sensing

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YOLO-FNC: An Improved Method for Small Object Detection in Remote Sensing Images Based on YOLOv7

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer Science 2024年第9期51卷 1281-1290页

作者： Dang, Lanxue Liu, Gang Hou, Yan-e Han, Hongyu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China

The detection algorithms of small objects in remote sensing images is often challenging due to the complex background and limited pixels. This can lead to reduced accuracy in detection and an increased number of missed small objects. So this paper introduces YOLOFNC, an enhanced network based on YOLOv7. To improve the model’s ability to capture features of small objects, an enhanced C3-Faster module based on the C3 module is designed and integrated into the YOLOv7 network. This module helps extract more features related to small objects. Additionally, we employ Normalized Wasserstein Distance (NWD) fusion GIoU as a novel loss function to refine the accuracy of network optimization weights and the small object regression framework. Furthermore, a coordinated attention (CA) mechanism is incorporated at strategic locations in the model to reduce redundant information in the feature layer and prevent the loss of important small object features. we conduct comparison experiments between YOLO-FNC and other commonly used object detection algorithms on DIOR, AITOD, and VisDrone datasets. The experimental results show that YOLO-FNC achieves 84.4% mAP on the DIOR dataset, 35.9% mAP on the AI-TOD dataset, and 52.6% mAP on the VisDrone dataset. Compared to YOLOv7 and other remote sensing object detection models, YOLO-FNC demonstrates better performance in object detection. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Proximity sensors

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Assisted Diagnostic Learning Based on Concept Lattice

Assisted Diagnostic Learning Based on Concept Lattice

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2021 International Conference on Signal processing and Communication Technology, SPCT 2021

作者： Shen, Xiajiong Li, Xingchen Han, Daojun Zhang, Yanfeng Henan Key Laboratory of Big Data Analysis and Processing Henan Univ. HA Kaifeng475004 China School of Computer and Information Engineering Henan Univ. HA Kaifeng475004 China Institute of Data and Knowledge Engineering Henan Univ. HA Kaifeng475004 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

With the continuous development of education, online education is becoming more and more popular with learners because of its convenience. However, online education has the problem of insufficient interaction with students. Students still need to rely on the students themselves to judge the lack of knowledge in practice tasks, which will greatly affect the learning efficiency. In this paper, we propose a method based on formal concept analysis to analyze the knowledge that may not be mastered, and a method of intensive practice for students. First of all, establishing the formal context of student error test questions, construct the concept lattice to calculate the degree of importance of the knowledge concept that may not be mastered, judge the student's mastery of the knowledge concept, and give feedback. Secondly, construct the concept lattice of the unpractised test set, and calculate the similarity of the concept extension set that meets the threshold, find similar topics and push them, and realize targeted training for students who have not mastered the knowledge concepts. © 2022 SPIE.

关键词： Students

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Application of CPU Parallel Computing in MATLAB Environment for analysis of Anti-Saturation and Gain Effects in PID Controller

Application of CPU Parallel Computing in MATLAB Environment ...

引用

data Driven Control and Learning Systems (DDCLS)

作者： Ruirui Huang Zhenhui Wu Yandong Hou Qianshuai Cheng Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Technology and Media University of Henan Kaifeng Kaifeng China School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the continuous increase in data size and model complexity, the computational workload has grown rapidly, posing a significant challenge to the capabilities of computer data processing and simulation calculations. Therefore, parallel programming based on multicore and cluster architectures has become one of the mainstream technologies to enhance program execution efficiency and numerical computation efficiency. The theoretical foundations of parallel program computing have been applied in various aspects of engineering applications and theoretical simulations. In this paper, a new parallel PID anti-integral saturation controller is designed for a second-order closed-loop control system of unmanned aerial vehicles (UAVs). It compares and analyzes the runtime, execution efficiency, and speedup ratio of parallel programs and general serial programs under the same scenario. The experimental results demonstrate that parallel computing significantly improves the simulation program's efficiency for PID controller anti-saturation control systems under identical scenarios, exhibiting a high speedup ratio on the existing computing platform. Additionally, this study consolidates common issues encountered in MATLAB parallel program design, offering valuable insights into overcoming challenges in this domain.

关键词： Runtime Parallel programming Multicore processing Process control Parallel processing Control systems Central processing Unit

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Research on access control decision model of Internet of Things based on attribute exploration

Research on access control decision model of Internet of Thi...

引用

2021 International Conference on Signal processing and Communication Technology, SPCT 2021

作者： Shen, XiaJiong Zhang, Peng Zhang, YanFeng Henan Key Laboratory of Big Data Analysis and Processing Henan Univ. HA Kaifeng475004 China School of Computer and Information Engineering Henan Univ. HA Kaifeng475004 China Institute of Data and Knowledge Engineering Henan Univ. HA Kaifeng475004 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

With the rapid development of the Internet of things, the problem of access control has become increasingly prominent. In order to solve the problems of high redundancy and low integrity caused by the traditional Role-Based Access Control model, this paper proposes an access control model of Internet of things based on attribute exploration. Firstly, the decision model is established based on attribute exploration algorithm, and finally merged into a complete decision model. The model has the function of dynamically modifying access rights in the process of building decision model. It is a new exploration in the field of access control model. © 2022 SPIE.

关键词： Internet of things

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PG-YOLO: A Lightweight Vehicle Detection Method for Drones

PG-YOLO: A Lightweight Vehicle Detection Method for Drones

引用

International Symposium on Parallel and Distributed processing with Applications, ISPA

作者： Yang Liu Tengyu Fan Kun Mei Liming Zhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Department of Clinical Laboratory Huaihe Hospital of Henan University Kaifeng China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the rapid development of drone technology, object detection technology is widely applied in the field of drone intelligent transportation. In response to the problems of low accuracy and high computational complexity in vehicle detection tasks on resource limited drones, this paper proposes a lightweight and efficient improved model PG-YOLO for drone vehicle detection based on the YOLOv7-tiny model. Firstly, replace the LeakyReLU activation function with the SiLU activation function to compensate for the lack of nonlinear expression in the LeakyReLU activation function, and improve the convergence speed and generalization of the model during training; Secondly, the feature extraction and fusion modules were optimized to enhance the network’s perception and capture capabilities of the target, improve detection accuracy, and reduce the number of parameters; Then, lightweight convolution PConv and GSConv are introduced, and a lightweight module PPC based on PConv is proposed to improve the network’s Neck layer, reducing the number of parameters and computation while maintain detection performance; Finally, the CBAM attention mechanism is introduced to improve the detection performance for the problem of dense vehicle targets. The improved network achieved an average detection accuracy of 60.1%, an increase of 12.1 percentage points compared to the original network, a reduction of 73.6% in parameter count, a reduction of 15.9% in computational complexity, and a detection speed of 154 FPS. Compared to the original network, the network is more lightweight and achieves higher detection accuracy, which can more effectively perform unmanned aerial vehicle detection tasks.

关键词： Training Accuracy Attention mechanisms Vehicle detection Computational modeling Transportation Feature extraction Autonomous aerial vehicles Computational complexity Drones

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