检索结果-内蒙古大学图书馆

Resonating with Cellular Pathways:Transcriptome Insights into Nonthermal Bioeffects of Middle Infrared Light Stimulation and Vibrational Strong Coupling on Cell Proliferation and Migration

引用

Research 2024年第4期2024卷 789-800页

作者： Xingkun Niu Zhongyu Wu Feng Gao Shaojie Hou Shihao Liu Xinmin Zhao Liping Wang Jun Guo Feng Zhang Quantum Biophotonic Lab Key Laboratory of Optical Technology and Instrument for MedicineMinistry of EducationSchool of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Department of Nuclear Medicine The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan HospitalJinan 250013China School of Radiology Shandong First Medical University and Shandong Academy of Medical SciencesJinan 250024China The School of Biomedical Engineering Guangzhou Medical UniversityPanyu DistrictGuangzhou 511436China

Middle infrared stimulation(MIRS)and vibrational strong coupling(VSC)have been separately applied to physically regulate biological systems but scarcely compared with each other,especially at identical vibrational frequencies,though they both involve resonant *** cell proliferation and migration as typical cell-level models,herein,we comparatively studied the nonthermal bioeffects of MIRS and VSC with selecting the identical frequency(53.5 THz)of the carbonyl *** found that both MIRS and VSC can notably increase the proliferation rate and migration capacity of *** sequencing results reflected the differential expression of genes related to the corresponding cellular *** work not only sheds light on the synergistic nonthermal bioeffects from the molecular level to the cell level but also provides new evidence and insights for modifying bioreactions,further applying MIRS and VSC to the future medicine of frequencies.

关键词： stimulation Middle identical

来源：评论

学校读者我要写书评

暂无评论

A Traffic Target Detection Algorithm Based on Hybrid Model Compression Method 4

A Traffic Target Detection Algorithm Based on Hybrid Model C...

引用

2022 4th International Conference on Electronic Engineering and Informatics, EEI 2022

作者： Yu, Wenze Liang, Quan Hu, Jinjing School of Computer Science and Mathematics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (纸本)9783800759231

With the development of the computer vision field, convolutional neural networks are widely used in object detection algorithms. However, the deep learning network model has high computational complexity, occupies a large storage space, and is difficult to deploy on low-computing devices. Aiming at the difficulty of deploying traffic target detection algorithms in practical scenarios, this paper proposes a target detection network compression method based on fusion of pruning and quantization. First, based on the Yolo series target detection algorithm, we perform an unstructured pruning operation with a variable threshold for the network model to be optimized, which can remove more information than the structure and reduce the parameters of the network model. Then, the network is quantized into 8-bit integer data through quantization-aware training to compress the network size. Experiments show that the optimized network model has less accuracy loss after variable threshold pruning and data quantization, and the model size is greatly compressed, which can achieve high detection capability even in the case of limited resources. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

RFSD-YOLO: An Enhanced X-Ray Object Detection Model for Prohibited Items

RFSD-YOLO: An Enhanced X-Ray Object Detection Model for Proh...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaotong Kong Aimin Li Wenqiang Li Zhiyao Li Yuechen Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

X-ray image detection is essential for ensuring public safety, but traditional methods rely heavily on human analysis and are relatively inefficient. To address this issue, this paper proposes a new object detection model called RFSD-YOLO, which is based on the YOLOv8 model and is specifically designed for detecting prohibited items in X-ray images. The model adopts the RFCAConv structure instead of the conventional convolutional operation. This allows for independent parameterisation among the convolutional kernels, enhancing the model's ability to capture and express potential prohibited item features. The neck section of the model includes the GSConv and VoVgscspdesigns, which aim to balance complexity and parameter size while maintaining detection performance and reducing computational burden. Additionally, we have introduced a dynamic head structure, DyHead, which replaces the traditional detection head design and improves detection accuracy without adding computational cost. The experimental results demonstrate that our enhanced model surpasses the current state-of-the-art object detection models in detecting prohibited items. Additionally, we introduce a simplified version of the RFSD-YOLOnano model to cater to resource-constrained environments. This streamlined model improves AP, AR, and mAP by 4.4%, 14.6%, and 3.4%, respectively, compared to YOLOv8n. This series of innovations not only validates the superiority of our model, but also provides new solutions in the field of automated detection of prohibited items for X-ray security screening.

关键词： Technological innovation Head Accuracy Computational modeling Object detection Data models Public security Complexity theory Security X-ray imaging

来源：评论

学校读者我要写书评

暂无评论

Semi-global Stereo Matching Algorithm Based on Census Transform 4

Semi-global Stereo Matching Algorithm Based on Census Transf...

引用

2022 4th International Conference on Electronic Engineering and Informatics, EEI 2022

作者： Hu, Jinjing Liang, Quan Yu, Wenze School of Computer Science and Mathematics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (纸本)9783800759231

Aiming at the problem that the center point of traditional Census transform window is vulnerable to the noise of outer boundary environment and the area of partial depth discontinuous area has low matching precision in stereo matching, a semi-global stereo matching algorithm based on anti-noise Census transform is proposed. Parallax graphs with a higher degree of match can be output. Firstly, by preprocessing the image, more information of the image can be retained and noise interference can be removed. Meanwhile, the Census algorithm is improved to overcome the dependence on the center pixel of the window and enhance the anti-interference of the initial generation value against noise. Secondly, in the cost aggregation stage, Sobel edge detection is used for cost aggregation. Then, the initial aberration is determined according to the rule that the winner is king. The last is the parallax optimization, through the left-right consistency detection strategy to reduce the false match points, improve the parallax accuracy of occlusion points, and finally get a fine parallax map. Compared with other stereo matching algorithms on Middlebury Vision website, the experimental results show that the proposed algorithm is superior to other stereo matching algorithms. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Geometrical optics

来源：评论

学校读者我要写书评

暂无评论

MPC-based Motion Control of Underwater Vehicle with Fixed Depth 4

MPC-based Motion Control of Underwater Vehicle with Fixed De...

引用

2022 4th International Conference on Electronic Engineering and Informatics, EEI 2022

作者： Yu, Wenjie Liang, Quan Xiong, Neng Hong, Chuanbo School of Computer Science and Mathematics Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (纸本)9783800759231

Aiming at the underactuated and nonlinear system of underwater vehicle, this paper studies the control of depth-fixing motion. The first is to analyze the kinematics and dynamics of the underwater vehicle, so as to establish the corresponding depth-fixing motion control model. Secondly, the controller based on the model predictive control algorithm is designed in the Simulink platform to realize the depth-fixing motion control and anti-disturbance test of the underwater vehicle. At the same time, the PID algorithm is introduced to compare the step response of the two. Finally, according to the simulation results, it can be concluded that the controller based on the MPC algorithm can effectively reduce the overshoot of the underwater vehicle, the control system can converge to the target value faster, and has strong anti-interference ability. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Controllers

来源：评论

学校读者我要写书评

暂无评论

An Efficient and Privacy-Preserving Data Aggregation Scheme Supporting Arbitrary Statistical Functions in IoT

引用

China Communications 2022年第6期19卷 91-104页

作者： Haihui Liu Jianwei Chen Liwei Lin Ayong Ye Chuan Huang College of Computer and Cyber Security Fujian Normal UniversityFuzhou 350117China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal UniversityFuzhou 350117China School of Computer Science and Mathematics Fujian University of TechnologyFuzhou 350118China Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhou 350117China

The Internet of Things(IoT)has profoundly impacted our lives and has greatly revolutionized our *** terminal devices in an IoT data aggregation application sense real-time data for the remote cloud server to achieve intelligent ***,the high frequency of collecting user data will raise people concerns about personal *** recent years,many privacy-preserving data aggregation schemes have been ***,most existing schemes cannot support either arbitrary aggregation functions,or dynamic user group management,or fault *** this paper,we propose an efficient and privacy-preserving data aggregation *** the scheme,we design a lightweight encryption method to protect the user privacy by using a ring topology and a random location *** this basis,the proposed scheme supports not only arbitrary aggregation functions,but also flexible dynamic user ***,the scheme achieves faulttolerant capabilities by utilizing a future data buffering *** analysis reveals that the scheme can achieve the desired security properties,and experimental evaluation results show the scheme's efficiency in terms of computational and communication overhead.

关键词： Internet of Things data aggregation privacy protection arbitrary aggregation functions

来源：评论

学校读者我要写书评

暂无评论

Class Constraint-based Discriminative Features Learning Algorithm for Palm Print and Palm Vein Fusion Recognition 7

Class Constraint-based Discriminative Features Learning Algo...

引用

7th International Conference on Signal and Image Processing, ICSIP 2022

作者： Zhang, Kunyan Wang, Huabin Yu, Chengnian Du, Mengli Tao, Liang Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei China Chaohu University School of Information Engineering Hefei China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

In recent years, hand-based multimodal biometrics has received a lot of attention in the field of pattern recognition due to its convenience, stability and inclusion of a wider range of recognition features. However, Some existing multimodal hand feature extraction methods extract the features of different modalities separately and then fuse them directly, mostly ignoring the correlation between the different modalities. In this paper, a multimodal biometric feature learning algorithm combining palm vein and palm print is proposed. The pixel difference vectors in multiple directions are first extracted by calculating the difference between each pixel in both modalities and its linear neighbour pixels. Then a simple and efficient feature learning algorithm is used, and the pixel difference vectors are projected into a low-dimensional binary code in a supervised manner. Finally, we perform a feature-level fusion of the learned binary features of palm veins and palm prints for multimodal hand feature recognition. The experimental results show that the method performs better than existing finger recognition methods. © 2022 IEEE.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Long and Recent Preference Learning with Recent-k Items Distribution for Recommender System

引用

IEEE Transactions on Multimedia 2025年

作者： Gao, Yongbiao Niu, Sijie Lv, Guohua Ling, Miaogen Geng, Xin Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Southeast University Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering China Nanjing University of Information Science and Technology School of Computer Science China Southeast University School of Computer Science and Engineering China

Reinforcement learning (RL) aims to formulate the recommendation task as a Markov decision process (MDP) and trains an agent to automatically learn the optimal recommendation policy from interaction trajectories through trial-and-error and reward mechanisms. However, most existing RL-based approaches overlook the correlation between items and the dynamics of user interests implied in temporally close interactions. Therefore, in this paper, we propose a reinforcement learning method that incorporates a "recent-k items"distribution to capture users' local preferences. Specifically, we model the output layer as two distinct branches. The "recent-k items"branch, formulated with a Kullback-Leibler divergence loss, learns the recent interests of users, whereas the other branch utilizes a one-step temporal difference error to capture long-term preferences. The proposed structure is integrated into deep Q-learning and actor-critics, resulting in two enhanced methods named R k Q and R k AC, respectively. Furthermore, a novel soft inter-reward is carefully designed to enhance the proposed method, and we theoretically prove the convergence of the proposed algorithm. We perform extensive experiments on two large real-world datasets and conduct further analysis of the influences of different action sequences, time intervals, and enhancement capabilities for state-of-the-art models. The experimental results demonstrate the efficacy of our proposed methods. © 1999-2012 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

MF-YOLO: Multimodal Fusion for Remote Sensing Object Detection Based on YOLOv5s

MF-YOLO: Multimodal Fusion for Remote Sensing Object Detecti...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Wenqiang Li Aimin Li Xiaotong Kong Yuechen Zhang Zhiyao Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Remote sensing object detection has flourished at a rapid rate of development. In remote sensing images (RSI), most algorithms perform well in detecting medium and large objects but perform poorly on small objects. This is because small objects occupy fewer pixels in the entire image, and the detector is more likely to be biased towards detecting large objects. In addition, the huge background in the image also introduces noise, causing many detection failures. Most approaches solve the problem of small object detection by increasing the depth of the neural network, but the results are not satisfactory. To solve the above problems, we propose a new method, MF-YOLO, that utilizes fused infrared (IR) and red-green-blue (RGB) images for remote sensing object detection. Through multimodal fusion (MF), we can obtain more positive information, thereby improving detection accuracy. In addition, we introduced the Bi-Level Routing Attention (BRA) module to improve the YOLOv5s model structure, and proposed a new loss function to enhance the model’s learning discrimination ability. Experimental results show that the accuracy of MF-YOLO on the VEDAI and NWPU VHR-10 datasets is 76.62% and 91.63% respectively (in terms of mAP @0.5 ). Compared with the existing state-of-the-art, the detection accuracy is significantly improved. In addition, our model has fewer weight parameters than YOLOv5s, allowing it to achieve relatively high near-real-time detection speed.

关键词： YOLO Accuracy Federated learning Noise Neural networks Prediction algorithms Routing

来源：评论

学校读者我要写书评

暂无评论

Dual-Channel Decoupling Marching with Density Relationship Global Fusion for Few-shot Object Detection 3

Dual-Channel Decoupling Marching with Density Relationship G...

引用

3rd International Conference on computer science, Electronic Information Engineering and Intelligent Control technology, CEI 2023

作者： Wang, Futian Yi, Yu Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China AHU-IAI Ai Joint Laboratory Anhui University Hefei China

ISBN: (纸本)9798350372052

Traditional deep learning approaches to object recognition typically require large datasets, and acquiring high-quality annotated data prohibitively expensive. Consequently, there has been a growing interest in few-shot object detection, which aims to classify and detect novel classification with only a limited number of samples. Existing methods do not fully exploit feature matching between support images and query images. We propose called the dual-channel decoupled matching with density relationship global fusion method for few-shot object detection. The dual-channel decoupled matching backbone network is specifically designed to mitigate the mutual interference between support features and query features, enabling the extraction of diverse information and improving the model's generalization capability. In order to perceive a more comprehensive feature information, the density relationship global fusion module utilizes feature information of different scales to obtain a more comprehensive feature representation. Extensive experiments conducted on the PASCAL VOC datasets validate the efficacy of our proposed method. © 2023 IEEE.

关键词： Object detection

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：