检索结果-内蒙古大学图书馆

An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network Encoder-decoder network

Detection of Small Underwater Organisms Based on Improved YOLOv8

学校读者我要写书评

暂无评论

IAENG International Journal of computer science 2024年第8期51卷 1020-1026页

作者： Miao, Liheng Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

The underwater environment is complex and diverse, making it challenging to locate aquatic organisms accurately. The precise identification of underwater animals is crucial for ecological research and fisheries management. Addressing the issue of inaccurate localization of small underwater targets, this study introduces a novel model, YOLOv8-2PCC, based on the YOLOv8 algorithm with improvements. First, to improve the efficiency of the YOLOv8 network, the C2F module in the original YOLOv8 network model was replaced with convolution to reduce the computational load of the model. Secondly, the up-sampling operator CARAFE is employed, which excels in capturing features at various scales. Finally, a small target detection layer has been incorporated to extract additional shallow features, effectively enhancing the model's ability to detect small targets. Utilizing the URPC dataset for training and testing, the results indicate that our proposed algorithm achieves a mean Average Precision (mAP) of 85.9%. Compared to YOLOv8n, there is a 4.4% improvement, effectively enhancing the accuracy of underwater organism detection in complex underwater environments. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Aquatic organisms

Research on Steel Surface Defects Detection Algorithms by YOLOv8 Based on Attention Mechanism

学校读者我要写书评

暂无评论

IAENG International Journal of computer science 2024年第9期51卷 1309-1315页

作者： Gao, ShiQuan Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

As deep learning advances, neural network technologies are increasingly penetrating the field of steel surface defect detection. To tackle the challenges of low accuracy and inadequate quality, we introduce CMS-YOLOv8s, a defect detection algorithm engineered for superior precision and efficiency. Initially, integrate the CBAM to enhance the network's focus on relevant information, allowing it to leverage multi-dimensional data like spatial and channel dimensions fully. Next, introduce the SPPFCSPC module to fuse feature information from different scales, expanding the model's receptive field and enhancing its feature extraction abilities. Finally, incorporate a small object detection head to boost the model's capability in identifying tiny targets, strengthening its ability to detect targets across different scales. Experimental findings show that CMS-YOLOv8s attains a detection accuracy of 70.4%, registering a significant 3.3% enhancement compared to the original YOLOv8s. This advancement distinctly improves defect detection accuracy across all categories. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Surface defects

Real-time all-frequency global llumination with radiance caching

学校读者我要写书评

暂无评论

Computational Visual Media 2024年第5期10卷 923-936页

作者： Youxin Xing Gaole Pan Xiang Chen Ji Wu Lu Wang Beibei Wang School of Software Shandong UniversityJinan 250101China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Global illumination(GI)plays a crucial role in rendering realistic results for virtual exhibitions,such as virtual car *** scenarios usually include all-frequency bidirectional reflectance distribution functions(BRDFs),although their geometries and light configurations may be *** allfrequency BRDFs in real time remains challenging due to the complex light *** approaches,including precomputed radiance transfer,light probes,and the most recent path-tracing-based approaches(ReSTIR PT),cannot satisfy both quality and performance requirements ***,we propose a practical hybrid global illumination approach that combines ray tracing and cached GI by caching the incoming radiance with *** approach can produce results close to those of ofline renderers at the cost of only approximately 17 ms at runtime and is robust over all-frequency *** approach is designed for applications involving static lighting and geometries,such as virtual exhibitions.

关键词： real-time global illumination all-frequency BRDFs Haar wavelets radiance caching

State space representation and phase analysis of gradient descent optimizers

学校读者我要写书评

暂无评论

science China(Information sciences) 2023年第4期66卷 140-154页

作者： Biyuan YAO Guiqing LI Wei WU School of Computer Science and Engineering South China University of Technology School of Computer Wuhan University

Deep learning has achieved good results in the field of image recognition due to the key role of the optimizer in a deep learning network. In this work, the optimizers of dynamical system models are established,and the influence of parameter adjustments on the dynamic performance of the system is proposed. This is a useful supplement to the theoretical control models of optimizers. First, the system control model is derived based on the iterative formula of the optimizer, the optimizer model is expressed by differential equations, and the control equation of the optimizer is established. Second, based on the system control model of the optimizer, the phase trajectory process of the optimizer model and the influence of different hyperparameters on the system performance of the learning model are analyzed. Finally, controllers with different optimizers and different hyperparameters are used to classify the MNIST and CIFAR-10 datasets to verify the effects of different optimizers on the model learning performance and compare them with related methods. Experimental results show that selecting appropriate optimizers can accelerate the convergence speed of the model and improve the accuracy of model recognition. Furthermore, the convergence speed and performance of the stochastic gradient descent(SGD) optimizer are better than those of the stochastic gradient descent-momentum(SGD-M) and Nesterov accelerated gradient(NAG) optimizers.

关键词： optimizer control model phase trajectory parameter adjustment classification dynamic performance

Research on Social Recommendation Algorithm Based on PSO_KFCM Clustering and CBAM Attention Mechanism of Graph Neural Networks

学校读者我要写书评

暂无评论

IAENG International Journal of computer science 2024年第8期51卷 936-948页

作者： Teng, Yue Yang, Kai School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In today's society, people increasingly need information acquisition due to the rapid development of science and technology and the consequent increase in available data. However, finding the information users need from this vast data has become challenging. To tackle this problem, recommending preferred information to users is becoming increasingly important. However, accurately recommending information by analyzing existing models such as GraphRec is still a challenging problem. A method called PSO_KFCM is proposed in this paper to solve this problem better. The technique combines Particle Swarm Optimization (PSO) with hybrid optimization and the kernel fuzzy C-means clustering technique to cluster similar recommendation data into one class. This way, the complexity and randomness of the recommendation data are reduced. It improves the speed and accuracy of the model prediction, which lays a solid foundation for the subsequent recommendation. Various factors will impact the recommendation process, and channel and spatial characteristics are essential. CBAM attention is added to the original attention mechanism to fully utilize these features in the recommendation data to enhance its performance. Furthermore, this paper proposes a social recommendation prediction method that combines CBAM attention and PSO_KFCM clustering and introduces a new social model called TTYGNN. The TTYGNN model optimizes the recommendation effect while maintaining the original advantages, enabling users to obtain the required information more quickly and accurately. To verify the effectiveness and practicality of the proposed model, extensive experimental comparisons were conducted on two widely used datasets. The results show that the TTYGNN model outperforms similar methods in all indicators, proving its superiority in information recommendation. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Fuzzy clustering

Effect of micro-Al_(2)O_(3) powders on oxidation and corrosion behaviors of low-carbon MgO-C refractories

学校读者我要写书评

暂无评论

Journal of Iron and Steel Research International 2024年第6期31卷 1522-1534页

作者： Yang Chen Zeng-yi Li Hao-bo Tan Yu-ran Fu Jun Ding Cheng-ji Deng Chao Yu School of Materials Science and Engineering Hunan Institute of TechnologyHengyang 421002HunanChina The State Key Laboratory of Refractories and Metallurgy Wuhan University of Science and TechnologyWuhan 430081HubeiChina

To solve the problem of poor high-temperature service performance caused by low carbonization of MgO-C refractories,low-carbon MgO–C refractories with excellent thermal shock,oxidation and corrosion resistances were successfully designed by using SiC whiskers as reinforcing phases and introducing micro-Al_(2)O_(3) powders as *** results indicated that the addition of micro-Al_(2)O_(3) powders optimized the internal structure of the material,like the columnar β-Si_(3)N_(4) with a stepped distribution and the mosaic structure formed between granular and flaky Mg_(2)SiO_(4),which synergistically strengthened and toughened the material and gave the material excellent mechanical properties and thermal shock ***,the cold modulus of rupture and cold crushing strength after thermal shock were increased by 4.1 and 20.3 MPa,***,the addition of micro-Al_(2)O_(3) powders promoted the formation of fine particles of Mg_(2)SiO_(4),MgAl_(2)O_(4) and MgO,as well as a dense protective layer of Mg_(2)SiO_(4) in the material under high-temperature ***,spinel and high-temperature solid solution were formed in the corrosion *** oxidation and corrosion resistances were greatly improved by 41%and 15%,respectively.

关键词： Low-carbon MgO-C refractory Oxidation resistance Corrosion resistance Thermal shock resistance Micro-Al_(2)O_(3)powder

Infrared Road Object Detection Based on Improved YOLOv8

学校读者我要写书评

暂无评论

IAENG International Journal of computer science 2024年第3期51卷 252-259页

作者： Luo, Zilong Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning 114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In recent years, infrared target detection has played a crucial role in intelligent transportation and assisted driving. Addressing the current issues of low detection accuracy, poor robustness, and missed detections in infrared image detection, we propose an improved infrared road traffic detection algorithm, YOLOv8-EGP, based on YOLOv8s. Firstly, we replace the original C2f module with the SCConv convolution module to extract feature information of different sizes, thereby enhancing the perception of local features in infrared images and connecting spatial relationships between them. Then, in the head part, we use the dyhead detection head and combine three different dimensions with multi-attention, improving the expression ability of the detection head for infrared targets without increasing computational complexity. Finally, we add a small target detection layer (min) to reduce missed detections of small targets in infrared images and improve the final detection accuracy. The conducted ablation experiments show that on the FILIR public dataset, compared to YOLOv8s, the YOLOv8-EGP algorithm increases mAP50 by 6.1%, and precision and recall also increase by 5.8% and 1.6%, respectively, indicating that the improved model can better adapt to infrared target detection, validating the effectiveness of this method. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Object detection

PeriodicMFD: A Periodic-based Framework for Multi-source Fault Diagnosis

学校读者我要写书评

暂无评论

IEEE Transactions on Transportation Electrification 2025年第3期11卷 7252-7260页

作者： Zheng, Jianbo Yang, Chao Zhang, Tairui Jiang, Bin Fan, Xuhui Wu, Xiao-Ming Shao, Haidong Hunan University College of Computer Science and Electronic Engineering Changsha China Macquarie University School of Computing Australia Sun Yat-sen University School of Computer Science and Engineering China Hunan University College of Mechanical and Vehicle Engineering Changsha China

Cross-speed bearing fault diagnosis based on multiple source domains and their data enables high-performance condition monitoring for variable-speed equipment, such as engines and turbines. Current multi-source methods typically employ a fixed-length sampling strategy to construct samples and then align the distributions of these samples from different domains. However, these methods neglect the inherent periodic characteristics of bearing data, resulting in incomplete or redundant periodic features in the samples. To address this challenge, we propose a periodic-based framework, PeriodicMFD, for multi-source cross-speed fault diagnosis, which ensures complete periodic information. Our PeriodicMFD framework begins with a periodic sampling strategy designed to construct periodic samples that effectively capture periodic features while maintaining their periodic integrity. Nevertheless, periodic samples from different domains exhibit inconsistencies at both the sample and domain levels. To reconcile these inconsistencies, we introduce sample-level matching to address inconsistencies in feature dimensions and fault patterns among samples from various domains. Additionally, we propose domain-level alignment to handle inconsistencies in space and distribution across different domains. Extensive experiments across three datasets highlight the effectiveness of the PeriodicMFD framework, with a stable average accuracy of 99.55%. © 2015 IEEE.

关键词： Fault detection