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bayesian-based analysis of sequence activity characteristics in the Bohai Rim region

APPLIED GEOPHYSICS 2024年 1-15页

作者： Bi, Jin-Meng Song, Cheng Cao, Fu-Yang Tianjin Earthquake Agcy Tianjin 300201 Peoples R China China Earthquake Adm Inst Geophys Beijing 100081 Peoples R China

Disaster mitigation necessitates scientific and accurate aftershock forecasting during the critical 2 h after an earthquake. However, this action faces immense challenges due to the lack of early postearthquake data and the unreliability of forecasts. To obtain foundational data for sequence parameters of the land-sea adjacent zone and establish a reliable and operational aftershock forecasting framework, we combined the initial sequence parameters extracted from envelope functions and incorporated small-earthquake information into our model to construct a bayesian algorithm for the early postearthquake stage. We performed parameter fitting and early postearthquake aftershock occurrence rate forecasting and effectiveness evaluation for 36 earthquake sequences with M >= 4.0 in the Bohai Rim region since 2010. According to the results, during the early stage after the mainshock, earthquake sequence parameters exhibited relatively drastic fluctuations with significant errors. The integration of prior information can mitigate the intensity of these changes and reduce errors. The initial and stable sequence parameters generally display advantageous distribution characteristics, with each parameter's distribution being relatively concentrated and showing good symmetry and remarkable consistency. The sequence parameter p-values were relatively small, which indicates the comparatively slow attenuation of significant earthquake events in the Bohai Rim region. A certain positive correlation was observed between earthquake sequence parameters b and p. However, sequence parameters are unrelated to the mainshock magnitude, which implies that their statistical characteristics and trends are universal. The bayesian algorithm revealed a good forecasting capability for aftershocks in the early postearthquake period (2 h) in the Bohai Rim region, with an overall forecasting efficacy rate of 76.39%. The proportion of "too low" failures exceeded that of "too high" failures, and the num

关键词： earthquake sequences bayesian algorithm model parameters correlation analysis effectiveness evaluation

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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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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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Developing novel dynamic prediction methods for survival time to analyze short-term and long-term progression of Alzheimer's disease

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2025年 165卷 103140-103140页

作者： Zhang, Chengfeng Chen, Shuyu Wang, Yanjie Xue, Pansheng Song, Yu Ren, Jiaqiao Zhou, Derun Chen, Zheng Southern Med Univ Sch Publ Hlth Dept Biostat Guangdong Prov Key Lab Trop Dis Res Guangzhou 510515 Peoples R China State Key Lab Organ Failure Res Guangzhou Peoples R China

Tracking and monitoring mild cognitive impairment (MCI) patients to intervene promptly at the imminent onset of Alzheimer's disease (AD) are crucial. However, existing dynamic survival prediction models for the conversion from MCI to AD are mostly based on hazard rates, which are less intuitive to interpret and require adherence to the proportional hazards assumption. To address this, we propose a bayesian joint model (JM) based on the time scale indicator of the restricted mean survival time (RMST), which can capture the trajectories of multiple longitudinal covariates and dynamically predict the patient time to event. Using Monte Carlo simulation, it can be demonstrated that the JM method has a better prediction performance compared with the static model. To predict the dynamic progression of AD in MCI patients at different stages, based on the landmark (LM) method and the JM method for RMST, we developed an LM-based model for short-term dynamic prediction (LM-ST model) and a JM-based model for long-term dynamic prediction (JM-LT model) utilizing the ADNI database. The internal and external validation results indicate that the predictive performance of the LM-ST and JM-LT models surpasses that of the static RMST model. Additionally, an online web tool for the two dynamic prediction models was created for clinical application. In summary, we propose a novel method and combined it with the existing LM method for AD progression, which improves the predictive power and provides a scientific basis for medical decision-making.

关键词： bayesian algorithm Alzheimer's disease Restricted mean survival time Dynamic prediction Short-term and long-term

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Method for Collision Relationship of Hydraulic Supports Considering Multilayer Sensing Errors

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IEEE SENSORS JOURNAL 2024年第19期24卷 30895-30908页

作者： Wang, Yirong Xie, Jiacheng Zheng, Ziying Wang, Yichen Wang, Xuewen Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Technol Shanxi Key Lab Fully Mechanized Coal Min Equipment Taiyuan 030024 Shanxi Peoples R China

To address the issues of low accuracy and insufficient information in the pose measurement of hydraulic support caused by complex working conditions, this article proposes a method for pose optimization and collision resolution. First, based on the structure and significance, the pose is divided into "three layers of base pose-one layer of internal pose." A mapping model of each layer's pose and sensing error is established to accurately resolve errors. Then, the bayesian algorithm is employed to infer the single pose under a normal distribution. A multimodel system is constructed through five steps: updating measurement, updating error, prior calculation, bayesian estimation, and pose correction, to optimize the complete pose of support. Subsequently, three highly credible virtual support scenes are constructed. Sensor information is input into the pose optimization scene to generate the pose distribution, which is synchronized to the collision-solving scene to visualize and solve collision probabilities. This probability is then fed back to the monitoring scene for warning judgment. Finally, various conditions are set for pose optimization and collision-solving experiments. The results show that the accuracy of the optimized support pose is enhanced by an average of 11.57% and the collision between supports is accurately simulated. This verifies the accuracy of the theoretical research and the bayesian algorithm. The approach is beneficial to warn the timely abnormal situation warnings, enhancing the reliability of underground support, and providing an important reference to support following control and adjustment.

关键词： Sensors Accuracy Hydraulic systems Bayes methods Monitoring Optimization Nonhomogeneous media bayesian algorithm collision hydraulic support optimization pose error sensor virtual reality (VR)

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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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Predicting the Law: Artificial Intelligence Findings from the IMF's Central Bank Legislation Database

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COMPUTATIONAL ECONOMICS 2024年 1-31页

作者： Alajmi, Khaled Deodoro, Jose Khan, Ashraf Moriya, Kei Int Monetary Fund Washington DC 20431 USA

Using the 2010, 2015, and 2020/2021 datasets of the IMF's Central Bank Legislation Database (CBLD), we explore artificial intelligence (AI) and machine learning (ML) approaches to analyzing patterns in central bank legislation. Our findings highlight that: (i) a simple Na & iuml;ve Bayes algorithm can link CBLD search categories with a significant and increasing level of accuracy to specific articles and phrases in articles in laws (i.e., predict search classification);(ii) specific patterns or themes emerge across central bank legislation (most notably, on central bank governance, central bank policy and operations, and central bank stakeholders and transparency);and (iii) other AI/ML approaches yield interesting results, meriting further research.

关键词： Central bank legislation Central banking Artificial intelligence Machine learning bayesian algorithm Boolean algorithm Central bank governance Law and economics E58 K00

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Green and energy-saving ecological renovation design of buildings based on energy consumption monitoring data

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 3227-3241页

作者： Li, Li Henan Tech Coll Construct Zhengzhou 450064 Henan Peoples R China

In order to achieve energy conservation and environmental protection of old buildings and reduce energy consumption, the design method of green energy conservation and ecological conversion of buildings based on energy consumption monitoring data is studied. A building energy consumption monitoring structure is designed to collect energy usage data of typical facilities in the area. According to the typical building energy consumption monitoring data, the building energy consumption prediction model is constructed by using the bayesian algorithm to obtain the energy consumption data of all buildings in the region. We set the building energy consumption comparison benchmark, calculated the energy conservation of the green energy-efficient ecological conversion design of housings according to the energy consumption data of all buildings in the region, took the maximisation of energy saving as the objective function, and built the green ecological transformation design model of buildings based on opportunity constraint planning. Different confidence levels are set within the model to reflect the user's tolerance for constraint violations and their choice of diversified comfort intervals. A particle congestion optimisation process is applied to fix the model, and the decision-making results of building green and energy-saving ecological transformation are obtained. The experimental results show that this method can effectively collect the energy consumption data of building energy usage equipment and accurately anticipate the energy usage data of other buildings in the region. Users can exchange economic and diversified energy consumption decisions by losing a certain degree of comfort and adjusting the constraint interval.

关键词： Energy consumption monitoring Green building Energy-saving and ecological transformation bayesian algorithm Energy consumption forecast Opportunity-constrained planning

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THE SECURITY AND PROTECTION SYSTEM OF ELECTROMECHANICAL EQUIPMENT IN SMART CAMPUS USING THE IMPROVED DATA MINING algorithm

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第6期25卷 5131-5141页

作者： He, Anyuan Nanjing Univ Aeronaut & Astronaut Informat Technol Ctr Nanjing 210016 Jiangsu Peoples R China

In order to improve the maintenance and management efficiency of campus electromechanical equipment and reduce or even avoid the safety risks brought by campus electromechanical equipment, this work uses the data mining algorithm to design the security and protection system of campus electromechanical equipment. First, this work constructs the campus electromechanical equipment classification model using the bayesian algorithm of data mining algorithm and designs a simulation experiment to verify the effect of the classification model. Then, the security and protection system for the campus electromechanical equipment is designed. It includes the system business process, system function design, system core module's function design and system implementation. Finally, a simulation experiment is designed to verify the system's performance. The results show that: (1) bayesian algorithm is superior to the K-Nearest Neighbor (KNN) algorithm in both classification effect and running time. (2) When the browser concurrency in the system increases, the server processor and memory usage also increases, but the value meets the expected requirements. It shows that the system has a certain browser concurrency-bearing capacity. Moreover, as the browser concurrency of the system increases, the response time of the test also increases, but the value meets the expected requirements. This work aims to improve the maintenance efficiency of campus electromechanical equipment and provide a reference for the safety protection work of electromechanical equipment in other enterprises or units.

关键词： data mining campus electromechanical safety bayesian algorithm K -Nearest Neighbor algorithm simulation experiment

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Negotiation mechanism of carbon emission quota trading process

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SUSTAINABLE PRODUCTION AND CONSUMPTION 2023年 39卷 336-344页

作者： Yang, Changhui Yang, Zhenfan Li, Yinxia Zhengzhou Univ Sch Business Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Mech & Power Engn Zhengzhou 450001 Peoples R China

Carbon emission quota trading is an effective method for realizing carbon neutrality. Negotiation can help to effectively resolve the differences and conflicts among the trading parties in the carbon emission quota trading process. For researching the negotiation mechanism of carbon emission quota trade, we construct the agent negotiation model that introduces the risk appetite of negotiator and bayesian algorithm. And then, the nego-tiation protocol is analyzed, including offer tactics, negotiation unity and termination condition. The experi-mental studies are given to simulate the negotiation of carbon emission quota trading process for demonstrating the validity of the negotiation model and tactics. This study demonstrates the impact of risk appetite on nego-tiation in the carbon emission quota trading. And can help to improve negotiation efficiency, achieve win-win and accurate matching among the trading parties of carbon emission quota.

关键词： Carbon emission quota trading Negotiation mechanism Negotiation model and tactics bayesian algorithm

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