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3rd International Conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Peng, Yanfei Li, Jincheng Guo, Xinyue School of Electronic and Information Engineering Liaoning University of Technology and Engineering Huludao125105 China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China

ISBN: (纸本)9781510681880

In recent years, with the rapid development of drone photography technology, drones have emerged as a new method for photography and observation. We have applied object detection technology to drone photography and proposed a YOLO-style 2D object detection network called YOLOvis based on the YOLOv8 architecture. We introduced the PatchFPN structure to improve the network's robustness in detecting small objects and proposed the TranConcat structure to scale features for enhanced usability. YOLOvis utilizes the challenging VisDrone data set as the model's task data set. With a slight increase in computational cost, the model achieves an accuracy of mAP50 33.7%, which is a 3.7% improvement compared to YOLOv8 of the same size and a 17.0% improvement compared to YOLOv5 of the same size. Additionally, to better tailor the model to specific needs, we have proposed YOLOvis models of different sizes. The code and pretrained models are in https://***/BarryGUN/bgyolo_v8_***. © 2024 SPIE.

关键词： Drones

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Visualizing Program Behavior: A Study of Enhanced Program Diagrams Using LLM 54

Visualizing Program Behavior: A Study of Enhanced Program Di...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Li, Ying Yang, Runze Gui, ShiJie Shi, Peng Huang, Xuefei Yang, Da Zhang, Xiaozhou Gai, Yiming School of Computer Science and Engineering Beihang University Beijing China International Innovation Institute Beihang University Key Laboratory of Data Science and Intelligent Computing Hangzhou China

ISBN: (纸本)9798350351507

This paper aims to address the difficulties faced by novice programmers in grasping code structure and execution flow, improving programming thinking, and pinpointing code errors with accuracy. It proposes providing students with program behavior diagrams based on large language models (LLMs) and visualization techniques to achieve personalized guidance. Specifically, these program behavior diagrams include programming thinking visualization diagrams and code vulnerability visualization diagrams. A programming thinking visualization diagram employs static code analysis to gather code structure information, combined with the structured chain-of-thought method to collectively optimize the LLM. This enables the LLM to explain each interpretable part of the code from top to bottom, detailing the programming concepts, and displaying them on a modularized code structure diagram. The code vulnerability visualization diagram primarily utilizes the fine-tuned LLM, optimizing it based on program analysis and clustering analysis methods to accurately identify vulnerabilities in student code and display them on a code flow diagram. Its feature is to visually display to students the error location, error information, and the impact of errors on program flow, rather than providing the programming answers. Lastly, through experiments and statistical analysis of actual teaching data, this paper serves a demonstration that the enhanced models used in the visualization diagram generation process have a noticeable effect on mainstream LLMs, and that visualization diagrams hold significant value for students at different stages of learning. © 2024 IEEE.

关键词： Students

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An Enhanced-Precision Bandgap Reference Incorporating Temperature-Compensation 3

An Enhanced-Precision Bandgap Reference Incorporating Temper...

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3rd International Symposium on Semiconductor and Electronic Technology, ISSET 2024

作者： Xiang, Tianzi Xu, Quanyuan Liu, Haorui Lu, Ying Southwest Forestry University College of Big Data and Intelligent Engineering Yunnan Kunming China Southwest Forestry University Key Laboratory of Forestry and Ecological Big Data State Forestry Administration Yunnan Kunming China Changchun University of Science and Technology School of Physics Jilin Changchun China

ISBN: (纸本)9798350390285

In this work, an Enhanced-Precision Bandgap Reference Incorporating Temperature-Compensation was proposed. Unlike traditional bandgap references, the reference voltage output of 0.701 V is realized by combining reference current and resistor. Meanwhile, an innovative Linear-piecewise current compensation was proposed, which aimed at optimizing the temperature drift of the bandgap reference. The simulation results shows that the proposed curvature-compensation technique improves the TC from 24.96 to 2.14 ppm°C in the temperature range of -40 °C to 120 °C;the PSRR of Enhanced-Precision Bandgap Reference at low frequency is 72.40 dB;the line regulation is about 0.08 %/V for input voltages between 3.2 and 3.7 V in standard 0.18 μm 1P6M CMOS process. The proposal of this new circuit provides a new idea for the realization of high- performance integrated circuits in the future. © 2024 IEEE.

关键词： Piecewise linear techniques

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State-of-Charge Estimation of Lithium Battery Based on Deep Residual Shrinkage Networks and a Variant Long Short Term Memory Neural Network

State-of-Charge Estimation of Lithium Battery Based on Deep ...

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第35届中国控制与决策会议

作者： Penghua Li Yihui Zhang Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications

Accurate state-of-charge(SOC) estimation,which is critical to ensuring the safe and reliable operation of battery management systems in electric vehicles,is still a challenging task due to sophisticated battery dynamics and ever-changing ambient *** paper proposes a method combining DRSN and ASTLSTM neural networks to estimate lithium batteries' state of charge(SOC).The proposed method can not only utilize the historical information of the data but also eliminate unimportant features by inserting soft thresholding into the deep *** reveal the performance of the proposed method,the results are compared with several other methods from the *** comparative experimental results show that the proposed method achieves a lower root mean square error(RMSE) of 0.83% with normal data and RMSE of 0.98% with noisy data,respectively.

关键词： Lithium Battery State of Charge Recurrent Neural Network Deep Residual Shrinking Network

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UFCNet: Unsupervised Network based on Fourier transform and Convolutional attention for Oracle Character Recognition 1

UFCNet: Unsupervised Network based on Fourier transform and ...

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1st Workshop on Machine Learning for Ancient Languages, ML4AL 2024

作者： Liu, Guoqi Zhou, Yanan Yang, Yiping Ru, Linyuan Liu, Dong Li, Xueshan College of Computer and Information Engineering Henan Normal University Henan China Oracle Bone Intelligent Computing Laboratory Henan Normal University Henan China Big Data Engineering Laboratory for Teaching Resource & Assessment of Education Quality Henan China

ISBN: (纸本)9798891761445

Oracle bone script (OBS) is the earliest writing system in China, which is of great value in the improvement of archaeology and Chinese cultural history. However, there are some problems such as the lack of labels and the difficulty to distinguish the glyphs from the background of OBS, which makes the automatic recognition of OBS in the real world not achieve the satisfactory effect. In this paper, we propose a character recognition method based on an unsupervised domain adaptive network (UFCNet). Firstly, a convolutional attention fusion module (CAFM) is designed in the encoder to obtain more global features through multi-layer feature fusion. Second, we construct a Fourier transform (FT) module that focuses on the differences between glyphs and backgrounds. Finally, to further improve the network’s ability to recognize character edges, we introduce a kernel norm-constrained loss function. Extensive experiments perform on the Oracle-241 dataset show that the proposed method is superior to other adaptive methods. The code will be available at https://***/zhouynan/UFCNet. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Self-decoupled Interpretable Prediction Framework for Highly-Variable Cloud Workloads 28th

A Self-decoupled Interpretable Prediction Framework for Hig...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Bingchao Shi, Xiaoyu Shang, Mingsheng Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China Chongqing School University of Chinese Academy of Sciences Chongqing400714 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9783031306365

Cloud workloads prediction plays a crucial role in the various tasks of cloud computing, such as resource scheduling, performance optimization, cost management, etc. However, current time series prediction methods suffer instability and inefficiency issues when addressing cloud workloads, due to the high variability of workload patterns and the high fluctuation within a workload. To address these issues, we propose DeIP4CW, a Self-Decoupled Interpretable Prediction framework for highly-variable Cloud Workloads. It can accurately forecast future job arrival rates in a cloud environment. The core idea of DeIP4CW is to first introduce the periodic and residual states as hidden variables to decouple complicated dependencies in cloud workload signals. Then it adopts a deep expansion learning framework with the block structure to perform workload prediction layer by layer. Each block consists of some periodic modules and some compensation modules. The periodic module with a self-attention mechanism can effectively capture the global trend of cloud workload, while the compensation module is employed to compensate for the local volatility information. Moreover, our two customized modules also have interpretable abilities, such as attributing the predictions to either global trends or local compensation. We conduct extensive experiments on the real-world cloud workload traces to evaluate the effectiveness of the proposed DeIP4CW. The experimental results demonstrate the DeIP4CW achieves significant improvements over the best baseline in most cases, and the error reduction can even reach up to 20.66%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud computing

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Blockchain-Based Multi-Cloud data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

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Efficient QoS data Prediction Based on Tensor Kernel Paradigm-Tensor Decomposition

Efficient QoS Data Prediction Based on Tensor Kernel Paradig...

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International Conference on Networking and Network Applications (NaNA)

作者： Hong Xia Jing Xu Qingyi Dong Hui Jia Yanping Chen Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

In the Mobile Edge computing (MEC) environment, the prediction efficiency is low when user recommendation is based on Quality of Service (QoS) data due to network environment and other factors. Traditional methods use Collaborative Filtering (CF) or tensor decomposition methods to mine the relationship between historical QoS data for prediction, but there are problems with prediction accuracy and efficiency. Given this, this paper proposes an efficient QoS data prediction method (CTT) combining tensor kernel paradigm-tensor decomposition. The method first introduces the tensor kernel norm to approximate the tensor rank function to better capture the spatio-temporal correlation of real QoS data; Second, tensor decomposition is combined to address inefficiencies. Experimental results on the public QoS dataset WS-Dream show that the method significantly improves the runtime while guaranteeing prediction accuracy and applies to scenarios with different data missing densities.

关键词： Tensors Accuracy Runtime Multi-access edge computing Computational modeling Quality of service Predictive models

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Note on a New Construction of Kantorovich Form q-Bernstein Operators Related to Shape Parameter λ

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Computer Modeling in Engineering & Sciences 2022年第3期130卷 1479-1493页

作者： Qingbo Cai Resat Aslan Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information ProcessingSchool of Mathematics and Computer ScienceQuanzhou Normal UniversityQuanzhou362000China Department of Mathematics Faculty of Sciences and ArtsHarran UniversitySanlıurfa63300Turkey

The main purpose of this paper is to introduce some approximation properties of a Kantorovich kind q-Bernstein operators related to B′ezier basis functions with shape parameterλ∈[−1,1].Firstly,we compute some basic results such as moments and central moments,and derive the Korovkin type approximation theorem for these ***,we estimate the order of convergence in terms of the usual modulus of continuity,for the functions belong to Lipschitz-type class and Peetre’s K-functional,***,with the aid of Maple software,we present the comparison of the convergence of these newly defined operators to the certain function with some graphical illustrations and error estimation table.

关键词： q-calculus (λ,q)-Bernstein polynomials order of convergence Lipschitz-type function Peetre’s K-functional

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LAGIM:A Label-Aware Graph Interaction Model for Joint Multiple Intent Detection and Slot Filling

LAGIM:A Label-Aware Graph Interaction Model for Joint Multip...

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第35届中国控制与决策会议

作者： Penghua Li Ziheng Huang Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications

Multi-intent spoken language understanding joint model can handle multiple intents in an utterance and is closer to complicated real-world scenarios,attracting increasing ***,existing research(1) usually focuses on identifying implicit correlations between utterances and one-hot encoding while ignoring intuitive and explicit original label characteristics;(2) only considers the token-level intent-slot interaction,which results in the limitation of the *** this paper,we propose a Label-Aware Graph Interaction Model(LAGIM),which captures the correlation between utterances and explicit labels' semantics to deliver enriched ***,a global graph interaction module is constructed to model the sentence-level interaction between intents and ***,we propose a novel framework to model the global interactive graph based on the injection of the original label semantics,which can fuse explicit original label features and provide global *** results show that our model outperforms existing approaches,achieving a relative improvement of 11.9% and 2.1%overall accuracy over the previous state-of-the-art model on the MixATIS and MixSnips datasets,respectively.

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