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IAENG International Journal of computer science 2024年第6期51卷 634-641页

作者： Qinrong, Li Chi, Ma Qiang, Guo Hui, Hu School of Computer Science and Software Engineering University of Science and Technology LiaoNing AnShan114051 China School of Computer Science and Engineering Huizhou University Huizhou516007 China

Images captured under severe weather conditions, such as haze and fog, suffer from image quality degradation caused by atmospheric particle diffusion. This degradation manifests as color fading, reduced contrast, and adversely affects the performance of various computer vision tasks. To address this, this paper presents an end-to-end feature fusion attention network (FFA-Net) designed to directly restore haze-free images. By incorporating the SSIM loss into the original loss function, the proposed method effectively captures the visual disparities between the estimated defogged image and the authentic haze-free image. Additionally, it mitigates the color distortion problem inherent in the original algorithm. To address the challenge of low brightness in input images, a low illumination enhancement module is introduced, seamlessly integrated with the FFA-Net defogging method. Subsequently, a comparative analysis of different defogging algorithms is conducted using two distinct foggy datasets. Multiple evaluation metrics are employed to assess the performance of these algorithms. The findings indicate that our algorithm significantly outperforms others in terms of objective indicators such as PSNR and SSIM, as well as visual effects. © (2024), (International Association of Engineers). All rights reserved.

关键词： Image enhancement

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Zinc-based fiber-shaped rechargeable batteries:Insights into structures,electrodes,and electrolytes

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Nano Research 2025年第1期18卷 278-293页

作者： Xuan Zhang Hongcheng Zhang Mengxiang Chen Jingqi Lu Evgeny Zhuravlev Jing Jiang Pin Liu Shengyang Dong Du Yuan Guoyin Zhu Lianbo Ma Yizhou Zhang Institute of Advanced Materials and Flexible Electronics(IAMFE) School of Chemistry and Materials ScienceNanjing University of Information Science and TechnologyNanjing 210044China School of Materials Science and Engineering Anhui University of TechnologyMaanshan 243002China College of Materials Science and Engineering Changsha University of Science and TechnologyChangsha 410004China

The rapid evolution of flexible wearable electronics has spurred a growing demand for energy storage devices,characterized by low-cost manufacturing processes,high safety standards,exceptional electrochemical performance and robust mechanical *** novel flexible devices,fiber-shaped batteries(FSBs)have emerged as prominent solutions exceptionally suited to future applications,owing to their unique one-dimensional(1D)architecture,remarkable flexibility,potential for miniaturization,adaptability to deformation and compatibility with the conventional textile *** the forefront research on fiber-shaped batteries,zincbased FSBs(ZFSBs)have garnered significant attentions,featured by the promising electrochemical properties of metallic *** enthusiasm is driven by the impressive capacity of Zn(820 mAh·g^(-1))and its low redox potential(Zn/Zn^(2+):-0.76 V *** hydrogen electrode).This review aims to consolidate recent achievements in the structural design,fabrication processes and electrode materials of flexible ***,we highlight three representative structural configurations:parallel type,twisted type and coaxial *** also place special emphasis on electrode modifications and electrolyte ***,we delve into the promising development opportunities and anticipate future challenges associated with ZFSBs,emphasizing their potential roles in powering the next generation of wearable electronics.

关键词： zinc-based batteries fiber-shape parallel type twisted type coaxial type

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Moisture Content’s Influence on Characteristics of DBD Plasma-Assisted Coal Combustion With Central Electrode Structure

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IEEE Transactions on Plasma science 2025年第4期53卷 669-677页

作者： Li, Ping Cheng, Xiaoyu Song, Haoyuan Anhui University of Science and Technology School of Electrical and Information Engineering Huainan232001 China Institute of Energy Hefei Comprehensive National Science Center Anhui Hefei230039 China Anhui University of Technology School of Electrical and Information Engineering Anhui Maanshan243032 China

The global energy structure is primarily dominated by fossil fuels, with coal being the main contributor to energy supply. This article proposes a combustion method based on the combination of nonequilibrium plasma generated by medium barrier discharge and water additives. The influence of different H2O atomization rates on discharge power, mass loss, and temperature variation during plasma-assisted coal combustion processes was investigated. The research findings indicate that, under a fixed power supply voltage amplitude of 13.5 kV, as the H2O atomization rate increases from 0 to 3.0 mL/min, the asymmetry of discharge current increases while discharge power decreases. When the H2O atomization rate is 1.2 mL/min, the mass loss, temperature, and combustion surface reach their maximum values. Excessive H2O inhibits coal combustion. Compared with the case of no H2O addition, at a power supply voltage amplitude of 13.5 kV and a H2O atomization rate of 1.2 mL/min, the combustion rate of coal significantly increases, and the combustion limit is extended. © 1973-2012 IEEE.

关键词： Coal combustion

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An Apricot Detection Algorithm in Complex Environments Based on Improved YOLOv7

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第12期51卷 2135-2144页

作者： Guo, Qiang Ma, Chi Hu, Hui School of Computer Science and Software Engineering University of Science and Technology LiaoNing AnShan114051 China School of Computer Science and Engineering Huizhou University Huizhou516007 China

Apricot detection is a prerequisite for counting and harvesting tasks. Existing algorithms face challenges in adapting to the impacts of complex environmental factors such as lighting variations, shadows, dense foliage, and the uneven distribution of samples in mechanized apricot harvesting. This paper proposes an enhanced model, YOLOv7-DC, based on YOLOv7, to address these challenges. YOLOv7-DC preprocesses diverse apricot tree samples to accommodate real-world harvesting detection scenarios. To improve model inference speed and detection accuracy, the detection network is redesigned with a new feature fusion method. DCNv2 is embedded within the efficient layer aggregation network (ELAN), and PConv is introduced to replace conventional convolutions, reducing the parameter impact of DCNv2. The training process incorporates the CBAM attention mechanism to enhance spatial and channel information. The ConvMixer architecture captures spatial and channel relationships transmitted to the detection head through the attention mechanism, improving the model’s detection accuracy for each specific classification sample. Experimental results show that YOLOv7-DC maintains good detection speed and recognition rates across various classification tasks. The improved model achieves a 6.2% increase in average detection accuracy compared to previous algorithms, with a 13% reduction in model parameters. YOLOv7-DC is better suited for handling imbalanced samples and complex environmental scenarios. © (2024), (International Association of Engineers). All rights reserved.

关键词： Apricot biloba detection Attention mechanism Feature fusion YOLOv7

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Individualized Data Generation in Personalized Federated Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6628-6642页

作者： Cai, Yunyun Xi, Wei Shen, Yuhao Sun, Cerui Wang, Shuai Gong, Wei Zhao, Jizhong Xi'an Jiaotong University Faculty of Electronic and Information Engineering Xi'an China Southeast University School of Computer Science and Engineering Nanjing China University of Science and Technology of China School of Computer science and Technology Hefei China

Most Personalized Federated Learning (PFL) algorithms merge the model parameters of each client with other (similar or generic) model parameters to optimize the personalized model (PM). However, the merged model parameters in these algorithms may fit low relevance data, thereby limiting the performance of PM. In this paper, we generate similar data for each client through the collaboration of a generic model (GM) on the server, rather than merging model parameters. To train a generator capable of generating data for all classes on the server without real data, we employ the GM as the discriminator in adversarial training with the generator. Additionally, we introduce a similarity assessment metric, which allows for the assessment of the similarity between local data and data from other classes. Nevertheless, the presence of non-IID data among clients can weaken the performance of the GM, consequently impacting the training of the generator and similarity assessment. To address this issue, we design a directive mechanism so that GM can be optimized during adversarial training without the need for additional training. The experimental results validate the superiority of our algorithm over state-of-the-art algorithms in terms of accuracy, loss, and convergence speed. © 2002-2012 IEEE.

关键词： Federated learning

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Secure Transmission Scheme for Blocks in Blockchain-Based Unmanned Aerial Vehicle Communication Systems

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computers, Materials & Continua 2024年第11期81卷 2195-2217页

作者： Ting Chen Shuna Jiang Xin Fan Jianchuan Xia Xiujuan Zhang Chuanwen Luo Yi Hong School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer Science Qufu Normal UniversityRizhao276826China School of Information Science and Technology Beijing Forestry UniversityBeijing100083China

In blockchain-based unmanned aerial vehicle(UAV)communication systems,the length of a block affects the performance of the *** transmission performance of blocks in the form of finite character segments is also affected by the block ***,it is crucial to balance the transmission performance and blockchain performance of blockchain communication systems,especially in wireless environments involving *** paper investigates a secure transmission scheme for blocks in blockchain-based UAV communication systems to prevent the information contained in blocks from being completely eavesdropped during *** our scheme,using a friendly jamming UAV to emit jamming signals diminishes the quality of the eavesdropping channel,thus enhancing the communication security performance of the source *** the constraints of maneuverability and transmission power of the UAV,the joint design of UAV trajectories,transmission power,and block length are proposed to maximize the average minimum secrecy rate(AMSR).Since the optimization problem is non-convex and difficult to solve directly,we first decompose the optimization problem into subproblems of trajectory optimization,transmission power optimization,and block length ***,based on firstorder approximation techniques,these subproblems are reformulated as convex optimization ***,we utilize an alternating iteration algorithm based on the successive convex approximation(SCA)technique to solve these subproblems *** simulation results demonstrate that our proposed scheme can achieve secure transmission for blocks while maintaining the performance of the blockchain.

关键词： Unmanned aerial vehicles blockchain finite blocklength block transmission alternating optimization

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AInvR:Adaptive Learning Rewards for Knowledge Graph Reasoning Using Agent Trajectories

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Tsinghua science and technology 2023年第6期28卷 1101-1114页

作者： Hao Zhang Guoming Lu Ke Qin Kai Du School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Multi-hop reasoning for incomplete Knowledge Graphs(KGs)demonstrates excellent interpretability with decent *** Learning(RL)based approaches formulate multi-hop reasoning as a typical sequential decision *** intractable shortcoming of multi-hop reasoning with RL is that sparse reward signals make performance *** mainstream methods apply heuristic reward functions to counter this ***,the inaccurate rewards caused by heuristic functions guide the agent to improper inference paths and unrelated object *** this end,we propose a novel adaptive Inverse Reinforcement Learning(IRL)framework for multi-hop reasoning,called AInvR.(1)To counter the missing and spurious paths,we replace the heuristic rule rewards with an adaptive rule reward learning mechanism based on agent’s inference trajectories;(2)to alleviate the impact of over-rewarded object entities misled by inaccurate reward shaping and rules,we propose an adaptive negative hit reward learning mechanism based on agent’s sampling strategy;(3)to further explore diverse paths and mitigate the influence of missing facts,we design a reward dropout mechanism to randomly mask and perturb reward parameters for the reward learning *** results on several benchmark knowledge graphs demonstrate that our method is more effective than existing multi-hop approaches.

关键词： Knowledge Graph Reasoning(KGR) Inverse Reinforcement Learning(IRL) multi-hop reasoning

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ElderEase AR: Enhancing Elderly Daily Living with the Multimodal Large Language Model and Augmented Reality 24

ElderEase AR: Enhancing Elderly Daily Living with the Multim...

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2024 International Conference on Virtual Reality technology, ICVRT 2024

作者： Song, Tianyu Liu, Zhengyi Zhao, Ruibin Fu, Jie Department of Computer Science and Technology School of Computer Science and Technology Intelligent Perception Group Anhui University Anhui Hefei China Chuzhou University Anhui Chuzhou China Department of Computer Science and Technology School of Computer Science and Technology Artificial Intelligence Group Anhui University Anhui Hefei China Department of Computer Science and Technology School of Information Engineering Intelligent Perception Group Chuzhou University Anhui Chuzhou China

ISBN: (纸本)9798400710186

Elderly individuals often face challenges in independent living due to age-related cognitive and physical decline. To address these issues, we propose an innovative Augmented Reality (AR) system, "ElderEase AR", designed to assist elderly users in their daily lives by leveraging a Multimodal Large Language Model (MLLM). This system enables elderly users to capture images of their surroundings and ask related questions, providing context-aware feedback. We evaluated the system’s perceived ease-of-use and feasibility through a pilot study involving 30 elderly users, aiming to enhance their independence and quality of life. Our system integrates advanced AR technology with an intelligent agent trained on multimodal datasets. Through prompt engineering, the agent is tailored to respond in a manner that aligns with the speaking style of elderly users. Experimental results demonstrate high accuracy in object recognition and question answering, with positive feedback from user trials. Specifically, the system accurately identified objects in various environments and provided relevant answers to user queries. This study highlights the powerful potential of AR and AI technologies in creating support tools for the elderly. It suggests directions for future improvements and applications, such as enhancing the system’s adaptability to different user needs and expanding its functionality to cover more aspects of daily living. © 2024 Copyright held by the owner/author(s).

关键词： Augmented reality

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YOLOv7-DSE: An Efficient Safety Equipment Detection Network

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第6期51卷 572-581页

作者： Ren, Jiaxin Cui, Wenhua Tao, Ye Shi, Tianwei School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Safety equipment detection is an important application of object detection, receiving widespread attention in fields such as smart construction sites and video surveillance. Significant progress has been made in object detection due to the rapid development of deep learning. Multi-scale targets and complex scenes increase the likelihood of false positives and missed detections, which can affect the accuracy of the detection. To address this issue, this study proposes YOLOv7-DSE. It is a small complex target scene detection network based on the improved YOLOv7. Also, we have created a private dataset of safety equipment. First, we enhanced the ELAN and MP backbone networks. Backbone is replaced by ordinary convolution by the depthwise separable convolution. We enabled the backbone network to extract deeper image features without increasing the amount of parameters and computation. Simultaneously, the model incorporates the EIOU loss function to improve its convergence speed and positioning effect. Secondly, we propose a new ELAN-SPD structure in the head network. Based on the ELAN structure, a space-to-depth convolutional layer is added to fully downsample the feature map, preserving all learnable features. Our network model can better detect objects with significant size differences faced with complex scenes. YOLOv7-DSE achieved the mAP of 82.38%, surpassing the original YOLOv7 with 2.64%. The YOLOv7-DSE model has a minor size compared to the baseline model. Our improvement has reduced the model parameters by 22.4%. © (2024), (International Association of Engineers). All rights reserved.

关键词： Object detection

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Towards machine-learning-driven effective mashup recommendations from big data in mobile networks and the Internet-of-Things

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Digital Communications and Networks 2023年第1期9卷 138-145页

作者： Yueshen Xu Zhiying Wang Honghao Gao Zhiping Jiang Yuyu Yin Rui Li School of Computer Science and Technology Xidian UniversityXi'an710126China School of Computer Engineering and Science Shanghai UniversityShanghai200444China School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China

A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually *** enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a *** emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of *** a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage ***,the question of how to determine the most suitable mashups from big data has become a challenging *** this paper,we propose a mashup recommendation framework from big data in mobile networks and the *** proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix *** employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the *** also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization *** then crawl through a real-world large mashup dataset and perform *** experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.

关键词： Mashup recommendation Big data Machine learning Mobile networks Internet-of-Things

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