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X-ray Security Inspection Prohibited Items Detection Model based on Improved YOLOv7-tiny

IAENG International Journal of Applied Mathematics 2024年第7期54卷 1279-1287页

作者： Teng, Wenzhao Zhang, Haigang Zhang, Yujun School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Shenzhen Polytechnic University Shenzhen China Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

X-ray security inspection for detecting prohibited items is widely used to maintain social order and ensure the safety of people’s lives and property. Due to the large number of parameters and high computational complexity, most current object detection models are challenging to deploy on portable mobile security inspection devices. Therefore, this paper proposes an improved YOLOv7-tiny model for application in prohibited item detection tasks. Firstly, the feature extraction backbone network is replaced with the lightweight GhostNet network to reduce computational complexity and improve detection speed. Secondly, the FPN in the Neck is replaced with BiFPN, further reducing computational complexity and memory access through skip connections. Finally, a lightweight CA attention mechanism is embedded between the Backbone and Neck layers, and the Focal-EIoU Loss function is employed to enhance the detection capability for small-sized items. Experimental results on the SIXray public dataset show a 14.8% reduction in model parameters, a 19.7% reduction in computational complexity, and a 15.9% reduction in volume after the improvements. The detection speed increases from 82.4 to 90.2, and the detection accuracy for prohibited items reaches 90.3%. The experimental results demonstrate that the improved model achieves overall lightweighting while maintaining a high detection rate and improving detection speed. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Computational complexity

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Joint multiple resource allocation for offloading cost minimization in IRS-assisted MEC networks with NOMA

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Digital Communications and Networks 2023年第3期9卷 613-627页

作者： Guang Chen Yueyun Chen Zhiyuan Mai Conghui Hao Meijie Yang Shuangshuang Han Liping Du School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovition School of Unversity of Science and Technology Beijing

Effective resource allocation can exploit the advantage of intelligent reflective surface(IRS)assisted mobile edge computing(MEC)***,it is challenging to balance the limited energy of MTs and the strict delay requirement of their *** this paper,in order to tackle the challenge,we jointly optimize the offloading delay and energy consumption of mobile terminals(MTs)to realize the delay-energy tradeoff in an IRS-assisted MEC network,in which non-orthogonal multiple access(NOMA)and multiantenna are applied to improve spectral *** achieve the optimal delay-energy tradeoff,an offloading cost minimization model is proposed,in which the edge computing resource allocation,signal detecting vector,uplink transmission power,and IRS phase shift coefficient are needed to be jointly *** optimization of the model is a multi-level fractional problem in complex fields with some coupled high dimension *** solve the intractable problem,we decouple the original problem into a computing subproblem and a wireless transmission subproblem based on the uncoupled relationship between different variable *** computing subproblem is proved convex and the closed-form solution is obtained for the edge computing resource ***,the wireless transmission subproblem is solved iteratively through decoupling the residual *** each iteration,the closed-form solution of residual variables is obtained through different successive convex approximation(SCA)*** verify the proposed algorithm can converge to an optimum with polynomial *** results indicate the proposed method achieves average saved costs of 65.64%,11.24%,and 9.49%over three benchmark methods respectively.

关键词： Mobile edge computing Intelligent reflective surface Task offloading Multiple resource allocation Non-orthogonal multiple access

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Dioscin from Polygonatum sibiricum induces apoptosis and autophagy in Ishikawa human endometrial cancer cell and in vivo

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Food science and Human Wellness 2024年第5期13卷 2601-2616页

作者： Xiaoli Li Runhui Ma Zhijing Ni Wei Wang Kiran Thakur Jianguo Zhang Zhaojun Wei School of Biological Science and Engineering North Minzu UniversityYinchuan 750021China School of Food and Biological Engineering Hefei University of TechnologyHefei 230009China School of Public Health Anhui University of Science and TechnologyHuainan 232001China

With an aim to comprehend the precise regulatory mechanism of dioscin against endometrial carcinoma(EC), we firstly extracted the components from Polygonatum sibiricum followed by identification and structural characterization. The anti-EC activity of dioscin was initially determined based on the inhibition of Ishikawa cell proliferation and tumor growth. The high-throughput sequencing data indicated that dioscin not only promoted apoptosis, including decrease of poly ADP-ribose polymerase(PARP) and B-cell lymphoma-2(Bcl-2) and increase of c-PARP and Bcl-2-associcated agonist of cell death(Bad), but also induced autophagy, including increase of autophagic lysosomes and LC3Ⅱ/LC3Ⅰ ratio. Mechanistic exploration suggested that dioscin induced autophagy and apoptosis through inhibition of PI3K/AKT/mTOR signaling pathway. Besides, the dioscin-regulated p53 pathway was mainly involved in autophagy induction. Furthermore, inhibition of Ishikawa cell autophagy was linked to dioscin-induced apoptosis. Our data suggest the immense potential of dioscin for the development of functional food for EC and related medical application.

关键词： Dioscin Ishikawa cell Apoptosis Autophagy PI3K/AKT p53

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Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction

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Digital Communications and Networks 2025年第2期11卷 537-546页

作者： Feng Zeng Zheng Zhang Jinsong Wu School of Computer Science and Engineering Central South UniversityChangsha410083China School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin541000China The Department of Computer Science Universidad de ChileSantiagoChile

In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service *** this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking ***,a Bi-LSTM-based model is proposed to predict the trajectories of *** service area is divided into several equal-sized *** the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory ***,we propose a scheduling strategy for delay optimization based on the vehicle trajectory *** the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task *** results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.

关键词： Vehicular edge computing Task offloading Vehicle trajectory prediction Delay minimization Bi-LSTM model

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DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of computer science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

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Long-term spatiotemporal variations of ammonia in the Yangtze River Delta region of China and its driving factors

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Journal of Environmental sciences 2025年第4期150卷 202-217页

作者： Jingkai Xue Chengzhi Xing Qihua Li Shanshan Wang Qihou Hu Yizhi Zhu Ting Liu Chengxin Zhang Cheng Liu School of Environmental Science and Optoelectronic Technology University of Science and Technology of ChinaHefei 230026China Key Lab of Environmental Optics&Technology Anhui Institute of Optics and Fine MechanicsHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Department of Precision Machinery and Precision Instrumentation University of Science and Technology of ChinaHefei 230026China Anhui Institute of Optics and Fine Mechanics Hefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes University of Science and Technology of ChinaHefei 230026China Institute of Physical Science and Information Technology Anhui UniversityHefei 230601China Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3) Department of Environmental Science and EngineeringFudan UniversityShanghai 200438China School of Earth and Space Sciences University of Science and Technology of ChinaHefei 230026China School of Environmental Science and Engineering Suzhou University of Science and TechnologySuzhou 215009China

This study focuses on the spatiotemporal distribution,urban-rural variations,and driving factors of ammonia Vertical Column Densities(VCDs)in China’s Yangtze River Delta region(YRD)from 2008 to *** data from the Infrared Atmospheric Sounding Interfer-ometer(IASI),Generalized Additive Models(GAM),and the GEOS-Chem chemical transport model,we observed a significant increase of NH_(3)VCDs in the YRD between 2014 and *** spatial distribution analysis revealed higher NH_(3)concentrations in the northern part of the YRD region,primarily due to lower precipitation,alkaline soil,and intensive agricul-tural ***_(3)VCDs in the YRD region increased significantly(65.18%)from 2008 to *** highest growth rate occurs in the summer,with an annual average growth rate of 7.2%during the period from 2014 to *** emissions dominated NH_(3)VCDs during spring and summer,with high concentrations primarily located in the agricultural areas adjacent to densely populated urban *** within several large urban areas have been discovered to exhibit relatively stable variations in NH_(3)*** rise in NH_(3)VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO_(2),as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem *** concentration changes of acidic gases contribute to over 80%of the interannual variations in NH_(3)*** emphasizes the crucial role of environmental policies targeting the reduction of these acidic *** emission control is urgent tomitigate environmental hazards and secondary particulate matter,especially in the northern YRD.

关键词： Yangtze River Delta Ammonia Spatiotemporal distribution Driving factors

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Enhanced Multi-Scale Object Detection Algorithm for Foggy Traffic Scenarios

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computers, Materials & Continua 2025年第2期82卷 2451-2474页

作者： Honglin Wang Zitong Shi Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Electrical&Computer Engineering University of Illinois at Urbana ChampaignUrbanaIL 61801USA

In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.

关键词： Deep learning object detection foggy scenes traffic detection YOLOv8

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An Improved Dehazing Algorithm Combining Dark Channel Prior and Artificial Bee Colony Algorithm

An Improved Dehazing Algorithm Combining Dark Channel Prior ...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Li, Zhenguo Ma, Lisheng Hou, Yibin Li, Jiaxin Tao, Tao Anhui University of Technology School of Computer Science and Technology China Chuzhou University School of Computer and Information Engineering China

ISBN: (纸本)9798350355581

Aiming to address significant issues like severe halo effects and excessive noise present in images processed by the traditional dark channel prior-based dehazing algorithm using fixed values, a proposed enhancement method leverages the characteristics of the artificial bee colony algorithm for parameter optimization. It seeks the optimal atmospheric light transmission factor values within different average image brightness ranges and integrates these values into the dark channel prior-based dehazing algorithm. Simultaneously, in estimating the atmospheric light, a median filtering algorithm replaces the original twostep minimum value filtering process. This improvement exhibits notable effectiveness in reducing halo effects and noise compared to the traditional dark channel prior-based dehazing algorithm. © 2024 IEEE.

关键词： Median filters

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Empowering Multimodal Large Language Models for Solving Cognitive Puzzles 24

Empowering Multimodal Large Language Models for Solving Cogn...

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2nd International Conference on Electronics, computers and Communication technology, CECCT 2024

作者： Zhang, Wentao School of Computer Science University of Science and Technology of China Anhui Hefei China

ISBN: (纸本)9798400710193

Multimodal Large Language Models have been showing their powerful ability for solving general vision-language tasks, such as image captioning, vision question answering, which usually on par with or even better than human does. However, when it comes to cognitive puzzles, we find it struggling for multimodal large language models to solve this type of tasks. In this paper, we study the capacity of MLLMs for solving cognitive puzzles. We experiments with cutting-edge open-sourced MLLMs such as Qwen2-VL and LLaMA 3.2 and compare their ability in solving cognitive puzzles at different aspects. After recognizing the shortcomings with careful examination, we develop a multi-step chain-of-thought based solution to enhance the MLLM to reasoning on the sophisticated image. To verify generalization, we include several sources of cognitive puzzles such as Raven’s Progressive Puzzles and CVR. © 2024 Copyright held by the owner/author(s).

关键词： Visual languages

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A significant wave height prediction method with ocean characteristics fusion and spatiotemporal dynamic graph modeling

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Acta Oceanologica Sinica 2024年第12期43卷 13-33页

作者： Xiao Yin Taoxing Wu Jie Yu Xiaoyu He Lingyu Xu Department of Computer Engineering and Science Shanghai UniversityShanghai 200444China School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China

Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave *** learning methods such as recurrent and convolutional neural networks have achieved good results in SWH ***,these methods do not adapt well to dynamic seasonal variations in wave *** this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural *** method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic ***,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern ***,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple ***,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three *** experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value ***,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.

关键词： significant wave height forecasting dynamic seasonal variation dynamic graph neural networks

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