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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Su, Yue Beijing Information Technology College Department of Education and Health Beijing China

ISBN: (纸本)9798350317022

With the expansion of vocational education enrollment, the number of students has been increasing year by year, and the management of students' life and learning in school is particularly important. Especially in the distribution of annual financial aid, the previous approach was based on student applications and jurisdictional certificates, which made it difficult to understand the true situation of students, and effectively identifying impoverished students has become a hot research topic. This article designs and implements a student affairs management system based on data mining, which integrates data mining ideas into the impoverished student management module, achieving accuracy in identifying impoverished students and completing the identification and management of impoverished students. By collecting students' family background data, extracting key indicators, and then using bayesian algorithm to predict the impoverished students, the true impoverished students can be identified. The results demonstrate the effectiveness of the proposed method in this paper. © 2023 IEEE.

关键词： Accurate Identification bayesian algorithm Data Mining

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A novel fault diagnosis method based on nonlinear-CWT and improved YOLOv8 for axial piston pump using output pressure signal

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ADVANCED ENGINEERING INFORMATICS 2025年 64卷

作者： Xia, Shiqi Huang, Weidi Zhang, Jie Cent South Univ Coll Mech & Elect Engn Changsha 410083 Peoples R China Zhejiang Univ Coll Mech Engn Hangzhou 310014 Peoples R China

Axial piston pumps are key components in hydraulic systems. Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system. Output pressure signals are commonly used for fault diagnosis due to their sensitivity to pump health conditions. However, these signals are non-stationary, containing transient and pulse components, making traditional time and frequency analysis methods insufficient for accurate fault diagnosis. To address this, a novel fault diagnosis method is proposed. First, a Nonlinear Continuous Wavelet Transform (Nonlinear-CWT) is developed to transforms one-dimensional output pressure signals into two-dimensional time-frequency images, amplifying key signal characteristics and reducing noise. Then, a Horizontal Deformable Convolutional Network (HDCN) is proposed to handle horizontal deformations in the images caused by varying sampling lengths, replacing standard convolution modules in the YOLO (You Only Look Once) model. Lastly, bayesian Optimization (BO) is employed to automatically optimize the hyperparameters, thereby producing a BHDCN-YOLO model. The experimental data of six health conditions with pump output pressure of 21 MPa is collected. Model performances are analyzed through the ablation experiments, comparison of other five deep learning model, and dataset with signal-to-noise ratios (SNR) of 30-50 dB. The results show that the BHDCN-YOLO model achieves an average accuracy of 97.38 % and inference speed of 0.9 ms. BHDCN-YOLO model accuracy improved by 20.2 % compared to the YOLO model. The adaptability experiment verified that HDCN-YOLO also has good recognition accuracy on datasets with additional sampling lengths. This study provides a novel method for more accurately diagnosing faults in axial piston pumps.

关键词： Fault diagnosis Continuous wavelet transform Horizontal deformable convolutional networks bayesian algorithm Deep learning

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A Markov chain Monte Carlo algorithm for bayesian dynamic signature verification

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2006年第1期1卷 22-34页

作者： Muramatsu, Daigo Kondo, Mitsuru Sasaki, Masahiro Tachibana, Satoshi Matsumoto, Takashi Waseda Univ Dept Elect Engn & Biosci Tokyo 1698555 Japan Univ Cambridge Dept Engn Signal Proc Grp Cambridge CB2 1PZ England

Authentication of handwritten signatures is becoming increasingly important. With a rapid increase in the number of people who access Tablet PCs and PDAs, online signature verification is one of the most promising techniques for signature verification. This paper proposes a new algorithm that performs a Monte Carlo based bayesian scheme for online signature verification. The new algorithm consists of a learning phase and a testing phase. In the learning phase, semi-parametric models are trained using the Markov Chain Monte Carlo (MCMC) technique to draw posterior samples of the parameters involved. In the testing phase, these samples are used to evaluate the probability that a signature is genuine. The proposed algorithm achieved an EER of 1.2% against the MCYT signature corpus where random forgeries are used for learning and skilled forgeries are used for evaluation. An experimental result is also reported with skilled forgery data for learning.

关键词： bayesian algorithm identification of persons Markov chain Monte Carlo signature trajectories

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Application of bayesian super-resolution imaging algorithm to micro-nano satellite images

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JOURNAL OF APPLIED REMOTE SENSING 2019年第3期13卷 030501-1-030501-8页

作者： Yu, Haixing Wang, Mingquan Xu, Lingyu Wang, Maohua Shanghai Univ Dept Comp Sci Shanghai Peoples R China Chinese Acad Sci Shanghai Adv Res Inst Shanghai Peoples R China

The need for high-resolution imaging becomes particularly important in remote sensing image applications, such as ground-object identification. We introduce a bayesian multi-frame super-resolution algorithm that efficiently improves the imaging resolution of our micro-nano carbon satellite images. We begin by presenting a theoretical overview of the algorithm, and subsequently we conduct experiments in two phases. In the first phase, we compare the performance of our algorithm with those of other similar algorithms using a set of reference images. In the second practical application phase, we apply all the algorithms considered to panchromatic images obtained from our civilian micro-nano carbon satellite. Our results indicate that the proposed algorithm significantly outperforms the other algorithms, affording higher image resolution and greater image detail. We believe that our approach can contribute to the further development of satellite imaging systems. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： remote sensing digital image processing super-resolution bayesian algorithm machine learning

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Regret Bounds for Log-Loss via bayesian algorithms

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IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第9期69卷 5971-5989页

作者： Wu, Changlong Heidari, Mohsen Grama, Ananth Szpankowski, Wojciech Ctr Sci Informat CSoI W Lafayette IN 47907 USA Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

We study sequential probability assignment in the context of online learning under logarithmic loss and obtain tight lower and upper bounds for sequential minimax regret. Sequential minimax regret is defined as the minimum excess loss over data horizon T that a predictor incurs over the best expert in a class, when the samples are presented sequentially and adversarially. Our upper bounds are established by applying bayesian averaging over a novel "smooth truncated covering" of the expert class. This allows us to obtain tight (minimax) upper bounds that subsume the best known non-constructive bounds in an algorithmic fashion. For lower bounds, we reduce the problem to analyzing the fixed design regret via a novel application of Shtarkov sum adapted to online learning. We demonstrate the effectiveness of our approach by establishing tight regret bounds for a wide range of expert classes. In particular, we fully characterize the regret of generalized linear function with worst Lipschitz transform functions when the parameters are restricted to a unit norm l(s) (s = 2) ball of dimension d. We show that the regret grows as T(d log T ) when d = O(Ts/(s+1)-?) for all ? > 0 (with precise constant 1 when d < e(o(log T ))) and O(Ts/(s+1)) when d = ?(Ts/(s+1)). Finally, we show that the bayesian approach may not always be optimal if the support of the prior is included in the reference class itself.

关键词： Sequential probability assignment online regression logarithmic loss bayesian algorithm Shtarkov sum

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Optimization of bayesian algorithms for multi-threshold image segmentation

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第4-5期24卷 2863-2877页

作者： Tian, Qiaoyu Xu, Wen Xu, Jin Sichuan Univ Sch Elect & Elect Informat Engn Jinjiang Coll Meishan Peoples R China

The bayesian optimization algorithm uses bayesian networks as the probability model of its solution space. Although the research on this algorithm has steadily developed, there are still some problems in its application process, such as excessive computational complexity. To solve various problems in bayesian algorithm, reduce its computational complexity, and enable it to better achieve image segmentation. The study chooses to improve the bayesian algorithm on the basis of immune algorithm, and solves the problem of computational complexity by reducing the number of bayesian network construction times, thereby improving the individual fitness of the population. Through simulation experiments, it has been shown that the average number of times the improved bayesian algorithm reaches the optimal value is 30, which is higher than the traditional algorithm's 20 times. Its excellent optimization ability searches for the optimal threshold to complete image segmentation. The improved bayesian optimization algorithm based on immune algorithm can effectively reduce computational complexity, shorten computational time, and improve convergence. And applying bayesian algorithm to image segmentation has broadened the application field of the algorithm and found new exploration directions for image segmentation.

关键词： bayesian algorithm image segmentation immune algorithm merit-seeking capability bayesian network multi threshold images algorithm application

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Evaluation algorithm of High Voltage Switchgear Based on bayesian Fusion

Evaluation Algorithm of High Voltage Switchgear Based on Bay...

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International Conference on Power System Technology (POWERCON)

作者： Xie, Jing Shu, Hongchun Zhang, Wenying Song, Qi Luo, Lingl Kunming Univ Sci & Technol Power Syst Kunming Yunnan Peoples R China

ISBN: (纸本)9781538664612

The failure rate of high voltage switchgear equipment is always high. In recent years, the problem of high failure rate of equipment has become more prominent due to the upgrading of old equipment. At the same time, the management of high-voltage switchgear and the forecast analysis of state trend are also important components of intelligent substation. For the state evaluation of electric equipment, there are diversity, randomness and fuzzy characteristics in fault phenomenon and failure mechanism. Based on the current assessment concept of "trend is heavier than the threshold", the algorithm that can reflect the variation trend of equipment failure and reflect the fault uncertainty is the main algorithm in the evaluation strategy. This paper analyzes the applicability of the three evaluation algorithms of fuzzy hierarchical theory, catastrophe theory and fuzzy cluster theory. Three of these algorithms adopt the fuzzy theory, to be able to satisfy the multidimensional input and the equipment under the state comprehensive evaluation, through fuzzy vector reflected device status interval and the trend of development, can adapt to the actual working conditions that some state information is missing. However, the characteristics of each algorithm inevitably have their own limitations, and the evaluation results will be different. To this end, this paper introduces information fusion concept, established comprehensive state evaluation model of switchgear based on the bayesian decision-level theory according to the characteristics of the different assessment methods and its applicability. It proposed bayesian fusion algorithm on decision layer based on the similarity. The validity of the algorithm is verified by the actual field data. The fusion algorithm can effectively improve the accuracy and reliability of the evaluation results, and enhance the fault tolerance performance of the evaluation system to achieve the function complementation between different sub-algorith

关键词： bayesian algorithm State Evaluation decision - level information fusion High Voltage Switchgear

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The bayesian CNN-LSTM Mixed Hybrid algorithm Model of the Photovoltaic Short-term Output Forecasting 3

The Bayesian CNN-LSTM Mixed Hybrid Algorithm Model of the Ph...

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3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Dong, Enchong Guo, Yuxi North China Electric Power University School of Control and Computer Engineering Beijing China Shenzhen College of International Education Shenzhen China

ISBN: (纸本)9798350393224

Due to the volatility, randomness, and intermittency of photovoltaic power generation, it is difficult to accurately forecast its output. This paper proposes a bayesian-optimized CNN-LSTM mixed neural model for a short-term photovoltaic output prediction model. First of all, the Xinjiang photovoltaic power station dataset is selected and preprocessed in this study. Secondly, the CNN method is applied for data feature extraction. And then the bayesian optimization algorithm is used to analyze the LSTM super parameter optimization. Finally, the data, after undergoing feature extraction, are input into the Bayes theorem-LSTM model for forecast generation. The results show that the proposed algorithm has realized the accurate prediction of photovoltaic power, getting better than most other single benchmark models. These findings not only highlight the improved efficacy of the proposed algorithm but also affirm its potential applicability and value for practical implementation. © 2023 IEEE.

关键词： bayesian algorithm Convolutional Neural Network algorithm Long Short-Term Memory algorithm Photovoltaic power output prediction

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Efficient recognition of Parkinson's disease mice on stepping characters with CNN

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SCIENTIFIC REPORTS 2025年第1期15卷 1-12页

作者： Luo, Yunsong Huang, Yiqi Fang, Guangzhan Tan, Ningping Tang, Yezhong Guangxi Univ Sch Mech Engn Nanning Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing Peoples R China Chinese Acad Sci Chengdu Inst Biol Chengdu Peoples R China Nanjing Med Univ Sch Basic Med Sci Nanjing Peoples R China

Parkinson's disease (PD), as the second most prevalent neurodegenerative disorder worldwide, impacts the quality of life for over 12 million patients. This study aims to enhance the accuracy of early diagnosis of PD through non-invasive methods, with the goal of enabling earlier intervention in the disease process. To this end, we constructed an open-field environment using flexible sensors under dark conditions, conducting experiments on a mouse model of Parkinson's disease alongside a normal control group. By processing footprint images collected in the absence of light-employing numerical area summation for noise reduction, adaptive enhancement algorithms based on pixel values, and a high-accuracy Convolutional Neural Network algorithm. And integrating motion data analysis, we achieved effective fusion of footprint images and behavioral data. After comprehensive analysis using decision-level fusion techniques and a Naive Bayes classifier, the results showed that the average classification accuracy for PD mice reached 96.56% when employing a multimodal data fusion strategy, demonstrating a significant advantage over using a single image recognition technique. This research not only highlights the substantial potential of applying multimodal data fusion strategies in the early detection of Parkinson's disease but also proves that such an approach can significantly improve diagnostic accuracy. The findings provide new insights into the early diagnosis of Parkinson's disease and other neurodegenerative disorders.

关键词： bayesian algorithm Footprint images Movement trajectories Parkinson's disease

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bayesian Sea Ice Detection algorithm for CFOSAT

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REMOTE SENSING 2022年第15期14卷 3569-3569页

作者： Li, Zhen Verhoef, Anton Stoffelen, Ad Royal Netherlands Meteorol Inst NL-3731 GA De Bilt Netherlands

This paper describes the adaptation of the bayesian sea ice detection algorithm for the rotating fan-beam scatterometer CSCAT onboard the China-France Oceanography Satellite (CFOSAT). The algorithm was originally developed and applied for fixed fan-beam and rotating pencil-beam scatterometers. It is based on the probability of the wind and ice backscatter distances from the measurements to their corresponding geophysical model functions (GMFs). The new rotating Ku-band fan-beam design introduces very diverse geometry distributions across the swath, which leads to three main adaptations of the algorithm: (1) a new probability distribution function fit for the backscatter distances over open sea;(2) a linear ice GMF as a function of incidence angle;(3) the separation of outer swath wind vector cells ((WVCs) number 1, 2, 41, 42) from the other WVCs to form two sets of probability distribution function fits for these two WVC groups. The results are validated against sea ice extents from the active microwave ASCAT and the passive microwave SSMI. The validation shows good agreement with both instruments, despite the discrepancies with SSMI during the melting season, and this discrepancy is caused by the lower sensitivity of the passive microwave to detect the ice at a low concentration with a mixed water/ice state, while the scatterometer is more tolerant regarding this situation. We observed that the sea-ice GMF regression between HH and VV sea-ice backscatter at low and high incidence angles decorrelates at around -12 dB (28 degrees) and -20 dB (50 degrees) and an experiment with truncated backscatter values at these incidence angles is executed, which significantly improves the year-long average sea ice extents. In conclusion, the adapted algorithm for CSCAT works effectively and yields consistent sea ice extents compared with active and passive microwave instruments. As such, it can, in principle, contribute to the long-term global scatterometer sea ice record, and as

关键词： sea ice bayesian algorithm CFOSAT scatterometer

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