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Research on the cross-platform information transmission method of industrial internet of things based on XML technology

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International Journal of Industrial and Systems engineering 2023年第3期43卷 317-330页

作者： Zhu, Changhong Pan, Tianci School of Computer Science and Engineering Guilin University of Aerospace Technology No. 2 Jinji Road Guangxi Guilin541004 China Department of Information Engineering Bowen College of Management Guilin University of Technology No. 317 Yanshan Street Guangxi Guilin541006 China

Aiming at problems of large error in data feature extraction and high congestion in the traditional information transmission methods, this paper proposes a cross-platform information transmission method of industrial internet of things based on XML technology. Based on the networked information features of extract, SUM function was used to complete the feature fusion. Then, the XML technology is used to obtain the optimal segmentation of tree, and the fitting training of tree data is carried out to realise the safe storage of information. Then, the information distribution probability is obtained according to the nature of XML file, so as to realise the cross-platform transmission of information. According to the simulation results, it can be seen that the data feature extraction error of this method is at least 2.1%, the sample data transmission time is always lower than 6 s, and the transmission process congestion is low, which fully proves its effectiveness. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Digital storage

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Optimization of Vegetable Replenishment and Pricing Strategies Based on Data Mining 4

Optimization of Vegetable Replenishment and Pricing Strategi...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Wang, Xiaolong Guan, Junlin Cao, Yang Zhao, Jing Chen, Taotao Jinxin, L. College of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350360721

In the digital era, vegetable sales forecasting and strategy optimization are important for superstores. Therefore, we developed an innovative integrated method combining Long Short-Term Memory Network (LSTM), Random Forest (RF), and Genetic Algorithm (GA) to optimize the pricing and replenishment decisions of vegetables on sale. First, the LSTM model was used to accurately predict the future vegetable sales trend to provide a scientific basis for decision-making. Next, the key factors in the sales data were analyzed using RF to identify the variables that affect the sales volume. Finally, GA was used to optimize the price and replenishment strategy based on the prediction results to maximize profits and balance market demand. This integrated approach improves the accuracy of sales prediction and optimizes the replenishment and pricing decision-making process through intelligent algorithms. Then, the operational efficiency of superstores improves significantly. The developed effectively responds to market changes, supports superstore management, and demonstrates its potential for applications in the retail industry. © 2024 IEEE.

关键词： Genetic algorithms

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FedQT: A Federated Learning Model Compression Algorithm Based on Quicksort and Top Avg Pruning

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 225-238页

作者： Zhang, Jin-Quan Xu, Hui-Xin Ma, Yun-Shen Ni, Li-Na College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

With the advent of the fourth industrial revolution, data ushered in explosive growth. Federated learning can protect users’ privacy and raw data from being known by third parties. Its client data is only trained locally and will not be uploaded to the server. However, the need to continuously transmit between the server and the client in federated learning increases communication overhead and communication delay, and the consumption of resources is very large. In order to improve the communication efficiency of the federation, we proposed an FedQT federated learning model compression algorithm based on pruning and quantization. Firstly, the fast sorting is used to preprocess the trained parameters. Secondly, the middle part of the processed parameters is averaged to obtain the pruning threshold avg(ω). Then, according to the obtained new threshold standard, the model performs Top Avg pruning based on median comparison. Finally, the k − means algorithm is used to cluster the parameters for further compression. In the simulation experiment, FedQT is compared with uncompressed and other compression algorithms, and the time complexity is analyzed. It is proved that FedQT can compress the trained model parameters to improve its communication efficiency while reducing the time overhead. The model can be compressed by more than one thousand times, and the model accuracy can be maintained above 97%, which is enough to prove that FedQT algorithm has good performance in the compression framework of federated learning. © 2024.

关键词： Efficiency

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CMDCF: an effective cross-modal dense cooperative fusion network for RGB-D SOD

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Neural Computing and Applications 2024年第23期36卷 14361-14378页

作者： Jia, XingZhao Zhao, WenXiu Wang, YuMei DongYe, ChangLei Peng, YanJun College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

The success of vision transformer demonstrates that the transformer structure is also suitable for various vision tasks, including high-level classification tasks and low-level dense prediction tasks. Salient object detection (SOD) is a pixel-level dense prediction task that simulates the most salient objects in human visual recognition scenarios. In recent years, depth images have been widely used for salient object detection. Compared with RGB SOD, the key point of RGB-D SOD is the effective fusion of depth information. As RGB-D SOD requires extracting depth features and fusing cross-modal information, additional computation is involved. However, except for lightweight models, most RGB-D SOD methods tend to obtain better prediction maps by consuming more computational resources. We propose a cross-modal dense cooperative fusion net, which provides state-of-the-art performance with less computation and parameters. We take advantage of the ability of the transformer structure to model long sequence dependencies to extract saliency features from RGB images. Since there is less information in the depth image than in the RGB image, it is not necessary to use the same structure in the depth stream. For the sake of reducing parameters and computation, we consider the asymmetric architecture. It is enough to meet our needs that deep features extracted by lightweight MobileV2Net. Our decoder can perform dense cooperative fusion of cross-modal information while decoding features. It can both effectively fuse cross-modal information and save computation. Comprehensive experiments on multiple benchmark datasets for RGB-D SOD show that compared with SOTA methods, our method performs much better with less computation and parameters. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Object detection

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PPDO:a privacy-preservation-aware delay optimization task-offloading algorithm for collaborative edge computing

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Frontiers of Information technology & Electronic engineering 2025年第1期26卷 27-41页

作者： Chao JING Jianwu XU College of Computer Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin 541004China

Although collaborative edge computing(CEC)systems are beneficial in enhancing the performance of mobile edge computing(MEC),the issue of user privacy leakage becomes prominent during task *** address this issue,we design a privacy-preservation-aware delay optimization task-offloading algorithm(PPDO)in a CEC *** considering location and usage pattern privacy protection,we establish a privacy task model to interfere with the edge server and ensure user *** address the extra delay arising from privacy protection,we subsequently leverage a Markov decision processing(MDP)policy-iteration-based algorithm to minimize delays without compromising *** simultaneously accelerate the MDP operation,we develop an extension that improves the PPDO by optimizing the action ***,a comprehensive simulation was conducted using the edge user allocation(EUA)*** results demonstrated that PPDO achieves an optimal trade-off between privacy protection and delay with a minimum delay compared with existing ***,we examined the advantages and disadvantages of improving PPDO.

关键词： Collaborative edge computing Task offloading Privacy protection Markov decision process

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Taking LM as the Brain: A Novel Approach Integrating Language Model and Generative Agent for Intelligent Decision Systems

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International Journal of Network Security 2024年第3期26卷 386-393页

作者： Yang, Jingkang Cai, Xiaodong Liu, Yining Chen, Mingyao Chang, Chin-Chen School of Information and Communication Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Research and Development Department Guilin Topintelligent Communication Technology Company Limited Guilin541004 China Department of Information Engineering and Computer Science Feng Chia University Taichung 407102 Taiwan

The Large Language Model (LLM) has demonstrated significant capabilities in intelligent robotics and Autonomous Driving(AD). Compared to traditional end-to-end models, decision reasoning in the form of language exhibits enhanced generalization and interpretability. To harness the inferential decision-making capabilities of large models more effectively, we propose a generative intelligent agent capable of translating various sensor signals into language descriptions, thereby utilizing Language Models (LMs) for decision-making. Furthermore, to address the practical deployment challenges of large models, we fine-tuned a GPT-2 model on an intelligent traffic corpus. The fine-tuned model performs competitively with LLMs in the domain of traffic decision-making. This approach can potentially deploy LMs directly on computationally constrained in-vehicle platforms to reduce communication latency. We evaluated the proposed method on a simulated dataset, and the results indicate that the pure language decision model surpasses the vector-based and fusion models. Moreover, our method exhibits significantly higher efficiency than LLMs. © (2024), (International Journal of Network Security). All rights reserved.

关键词： Decision making

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Ca_(2)MnO_(4)-layered perovskite modified by NaNO_(3)for chemical-looping oxidative dehydrogenation of ethane to ethylene

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Chinese Journal of Chemical engineering 2024年第4期68卷 53-64页

作者： Weixiao Ding Kun Zhao Shican Jiang Zhen Huang Fang He College of Chemistry and Bioengineering Guilin University of TechnologyGuilin 541006China Guangzhou Institute of Energy Conversion Chinese Academy of SciencesGuangzhou 510640China School of Chemistry and Chemical Engineering Nanjing University of Science and TechnologyNanjing 210094China

Chemical-looping oxidative dehydrogenation(CL-ODH)is a process designed for the conversion of alkanes into olefins through cyclic redox reactions,eliminating the need for gaseous O_(2).In this work,we investigated the use of Ca_(2)MnO_(4)-layered perovskites modified with NaNO_(3) dopants,serving as redox catalysts(also known as oxygen carriers),for the CL-ODH of ethane within a temperature range of 700-780℃.Our findings revealed that the incorporation of NaNO_(3) as a modifier significantly-nhanced the selectivity for-thylene generation from Ca_(2)MnO_(4).At 750℃and a gas hourly space velocity of 1300 h^(-1),we achieved an-thane conversion up to 68.17%,accompanied by a corresponding-thylene yield of 57.39%.X-ray photoelectron spectroscopy analysis unveiled that the doping NaNO_(3) onto Ca_(2)MnO_(4) not only played a role in reducing the oxidation state of Mn ions but also increased the lattice oxygen content of the redox ***,formation of NaNO_(3) shell on the surface of Ca_(2)MnO_(4) led to a reduction in the concentration of manganese sites and modulated the oxygen-releasing behavior in a step-wise *** modulation contributed significantly to the enhanced selectivity for ethylene of the NaNO_(3)-doped Ca_(2)MnO_(4) *** findings provide compelling evidence for the potential of Ca_(2)MnO_(4)-layered perovskites as promising redox catalysts in the context of CL-ODH reactions.

关键词： Chemical-looping oxidative dehydrogenation Ethane Ethylene NaNO_(3)-doped Ca_(2)MnO_(4)redox catalyst Layered perovskites

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Performance issue monitoring, identification and diagnosis of SaaS software:a survey

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Frontiers of computer science 2025年第1期19卷 71-95页

作者： Rui WANG Xiangbo TIAN Shi YING College of Mining and Safety Engineering Shandong University of Science and TechnologyQingdao 266590China School of Computer Science Wuhan UniversityWuhan 430072China

SaaS (Software-as-a-Service) is a service model provided by cloud computing. It has a high requirement for QoS (Quality of Software) due to its method of providing software service. However, manual identification and diagnosis for performance issues is typically expensive and laborious because of the complexity of the application software and the dynamic nature of the deployment environment. Recently, substantial research efforts have been devoted to automatically identifying and diagnosing performance issues of SaaS software. In this survey, we comprehensively review the different methods about automatically identifying and diagnosing performance issues of SaaS software. We divide them into three steps according to their function: performance log generation, performance issue identification and performance issue diagnosis. We then comprehensively review these methods by their development history. Meanwhile, we give our proposed solution for each step. Finally, the effectiveness of our proposed methods is shown by experiments.

关键词： SaaS software performance log generation performance issue identification performance issue diagnosis

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Synthesis and Mechanical Properties of a 3D Printable Photocurable Resin Composed of Rosin and Triethylene Glycol Dimethacrylate 4th

Synthesis and Mechanical Properties of a 3D Printable Photoc...

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4th International Symposium on Automation, Mechanical and Design engineering, SAMDE 2023

作者： Xu, Hanrui Huang, Shijie Ding, Lei Li, Qiang Song, Jianhui Sun, Danyang Tang, Chuanyang Liu, Mengyu Liu, Yan Chen, Shuoping College of Materials Science and Engineering Guilin University of Technology Guilin541004 China

ISBN: (纸本)9783031626630

In this study, we present an innovative approach aimed at developing cost-effective 3D printing photocurable materials with significantly enhanced mechanical properties, alongside a new method for utilizing rosin (RO) with high added value. This progress was achieved by incorporating RO into a photocurable resin primarily composed of triethylene glycol dimethacrylate (TEGDMA). The resulting RO/TEGDMA resin enables high-quality molding with light-curing 3D printers. Mechanical testing demonstrated a substantial improvement in the toughness of the TEGDMA-based resin with the addition of RO. The optimized composition of the RO/TEGDMA 3D printing composite, with a mass ratio of RO:TEGDMA = 1:10, exhibited exceptionally favorable mechanical properties, including a remarkable tensile strength of 29.94 MPa, an elongation at break of 10.28%, and an impressive impact strength of 10.81 kJ/m2, surpassing those of pure TEGDMA formulations. Thus, the conversion of RO into the RO/TEGDMA 3D printing composite presents a novel approach for the value-added utilization of RO resources and holds significant promise in developing low-cost, mechanically enhanced 3D printable photocurable resins. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Resins

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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

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computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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