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Adoption of machine learning algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

FRONTIERS IN NEUROROBOTICS 2021年 14卷 620378页

作者： Xu, Teng Tang, Lijun Shanghai Jiao Tong Univ Dept Phys Educ Shanghai Peoples R China Shanghai Normal Univ Phys Educ Coll Shanghai Peoples R China

In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

关键词： machine learning algorithm intelligent robot basketball training sports injury athlete injury prevention

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An explainable machine learning algorithm for risk factor analysis of in-hospital mortality in sepsis survivors with ICU readmission

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021年 204卷 106040-106040页

作者： Jiang, Zhengyu Bo, Lulong Xu, Zhenhua Song, Yubing Wang, Jiafeng Wen, Pingshan Wan, Xiaojian Yang, Tao Deng, Xiaoming Bian, Jinjun Naval Med Univ Changhai Hosp Fac Anesthesiol 168 Changhai Rd Shanghai 200433 Peoples R China Naval Med Univ Naval Med Ctr Dept Anesthesiol Shanghai 200052 Peoples R China Heal Sci Technol Co Ltd BDA 1606Tower 52 Rong Hua South Rd Beijing 100176 Peoples R China

Background and objective: Patients who survive sepsis in the intensive care unit (ICU) (sepsis survivors) have an increased risk of long-term mortality and ICU readmission. We aim to identify the risk factors for in-hospital mortality in sepsis survivors with later ICU readmission and visualize the quantitative relationship between the individual risk factors and mortality by applying machine learning (ML) algorithm. Methods: Data were obtained from the Medical Information Mart for Intensive Care III (MIMIC-III) database for sepsis and non-sepsis ICU survivors who were later readmitted to the ICU. The data on the first day of ICU readmission and the in-hospital mortality was combined for the ML algorithm modeling and the SHapley Additive exPlanations (SHAP) value of the correlation between the risk factors and the outcome. Results: Among the 2970 enrolled patients, in-hospital mortality during ICU readmission was significantly higher in sepsis survivors ( n = 2228) than nonsepsis survivors ( n = 742) (50.4% versus 30.7%, P < 0.001). The ML algorithm identified 18 features that were associated with a risk of mortality in these groups;among these, BUN, age, weight, and minimum heart rate were shared by both groups, and the remaining mean systolic pressure, urine output, albumin, platelets, lactate, activated partial thromboplastin time (APTT), potassium, pCO2, pO2, respiration rate, Glasgow Coma Scale (GCS) score for eye-opening, anion gap, sex and temperature were specific to previous sepsis survivors. The ML algorithm also calculated the quantitative contribution and noteworthy threshold of each factor to the risk of mortality in sepsis survivors. Conclusion: 14 specific parameters with corresponding thresholds were found to be associated with the in-hospital mortality of sepsis survivors during the ICU readmission. The construction of advanced ML techniques could support the analysis and development of predictive models that can be used to support the decisions and

关键词： Sepsis Sepsis survivor Readmission machine learning algorithm Predictive modeling Critical care

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Football Player Injury Full-Cycle Management and Monitoring System Based on Blockchain and machine learning algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 41-41页

作者： Pu, Chunwang Zhou, Jun Sun, Jian Zhang, Jianpeng Yuxi Normal Univ Sch Phys Educ Yuxi 653100 Peoples R China Coll Art & Sci Kunming Sch Phys Educ Kunming 650201 Peoples R China Yunnan Agr Univ Sch Phys Educ Kunming 650201 Peoples R China

Football injuries are the most common factor affecting a football player's performance, and the last thing a football player wants. To understand the causes of football players' injuries and how to recover sports injuries most efficiently, the football players' injuries were managed and monitored throughout the whole cycle. However, the traditional football player injury cycle management and monitoring system are not only insecure in data storage, but more importantly, it lacks intelligent analysis of the collected data. With the continuous development of blockchain and machine learning technologies, blockchain technology is used to collect, store, clean, mine and visualize the full-cycle data of football players' injuries, and machine learning is used to provide intelligent solutions for football players' injury recovery. This paper compared the football player's injury full-cycle management and monitoring system based on blockchain and machine learning algorithm with the traditional football player's injury management and monitoring system. The experimental results showed that the average self-processing capacity of the football player injury MMS based on blockchain and ML algorithm was 70%, while the average self-processing capacity of the traditional football player injury management and monitoring system was 50%. Therefore, the application of blockchain and machine learning algorithm in the football player's injury full-cycle management and monitoring system can effectively improve the system's self-processing ability.

关键词： Full-cycle management and supervision system Football player injury machine learning algorithm Blockchain technology Support vector machine

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A machine learning algorithm-enhanced mathematical simulation based on Carrera unified formulation to estimate fundamental frequency of the FG origami-enabled metamaterial composite system surrounded by auxetic concrete foundation

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年

作者： Wei, Qiujie Li, Jialing Cheng, Xi Rehman, Ateekh Ur Chengdu Polytech Chengdu Peoples R China Chongqing Youth Vocat & Tech Coll Sch Artificial Intellegence Chongqing Peoples R China Zhuzhijian Technol Chongqing Co Ltd Chongqing Peoples R China King Saud Univ Coll Engn Dept Ind Engn Riyadh Saudi Arabia

This study investigates the nonlinear dynamic behavior and natural frequencies of skew plates integrated with FG (Functionally Graded) origami-enabled metamaterials and supported by auxetic concrete foundations. The analysis is performed using the Carrera unified formulation (CUF), which provides a robust and accurate method for assessing the flexural response of complex plate systems. The formulation accounts for geometric nonlinearity, material gradation, and skew angles, capturing both the nonlinear natural frequencies and dynamic deflections. The inclusion of auxetic concrete foundations enhances the structural performance due to their negative Poisson's ratio, which offers improved energy absorption and deformation characteristics. To further validate the accuracy and reliability of the proposed method, the results are verified through comparison with existing literature and are supplemented by a novel fuzzy decision tree algorithm as a machine learning tool. The fuzzy algorithm aids in automating the verification process and identifying patterns in the dynamic response, ensuring computational efficiency and robustness. Results demonstrate the significant influence of skew angles, and metamaterial properties on the dynamic behavior of skew plates. The study highlights the advantages of FG origami-enabled metamaterials in enhancing structural stiffness and reducing dynamic deflections, making them suitable for advanced engineering applications. This research bridges the gap between computational mechanics and machine learning, providing a comprehensive approach to analyzing and verifying the nonlinear dynamic performance of skew plates with auxetic foundation support.

关键词： Carrera unified formulation skew plate FG origami-enabled metamaterials auxetic concrete foundation nonlinear natural frequency and dynamic deflection machine learning algorithm

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Utilizing machine learning algorithm, cloud computing platform and remote sensing satellite data for impact assessment of flood on agriculture land

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CURRENT SCIENCE 2023年第8期125卷 886-895页

作者： Kumar, Himanshu Kumar, Rohan Dutta, Sujay Singh, Magan Karwariya, Sateesh Kr. ICAR Natl Dairy Res Inst Karnal 132 001 Haryana India Lovely Profess Univ Phagwara 144001 India Indian Space Res Org Space Applicat Ctr Ahmadabad 380015 India Govt Gujarat Commissionerate Rural Dev Gandhinagar 382010 India

Floods are one of the most devastating natural disasters that cause immense damage to life, property and agriculture worldwide. Recurring floods in Bihar (a state in eastern India) during the monsoon season impact the agro-based economy, destroying crops and making it difficult for farmers to prepare for the next season. To mitigate the impact of floods on the agricultural sector, there is a need for early warning systems. Nowadays, remote sensing technology is used extensively for monitoring and managing flood events, which is also used in the present study. The random forest (RF) machine learning (ML) algorithm has also been used for land-use classification, and its output is used as an input for flood impact assessment. Here, we have analysed the flood extents and their impact on agriculture using Sentinel-1 form. The present study shows that floods severely impacted a large part of Bihar during the monsoon seasons of 2020 and 2021. About 701,967 ha of land (614,706 ha agricultural land) in 2020 and 955,897 ha (851,663 ha agricultural land) in 2021 were severely flooded. An to visualise the results, which can help the government authorities prioritize relief and rescue operations.

关键词： Agriculture cloud computing platforms floods machine learning algorithm remote sensing data

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QAM-DMT of Hybridly Integrated EAM-DFBLD Chip-on-Board With Adaptive machine learning algorithm for 149.6-Gbit/s BtB and 138-Gbit/s 10-km-SMF Transmission

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IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 2022年第4期28卷 1页

作者： Wu, Wei-Li Cheng, Chih-Hsien Lin, Huang-Shen Lin, Yu-Hong Su, Borching Lin, Gong-Ru Natl Taiwan Univ Grad Inst Photon & Optoelect Taipei 10617 Taiwan Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan Univ Tokyo Res Ctr Adv Sci & Technol Tokyo 1530041 Japan

By using the generalized frequency division multiplexing (GFDM) and the machine learning algorithm for self-feedback amplitude/phase pre-distortion, the electro-absorption modulator integrated distributed-feedback semiconductor laser diode (EAM-DFBLD) is encoded with improved transmission quality and capacity. Both back-to-back (BtB) and 10-km single-mode fiber (SMF) transmissions are preliminarily demonstrated for inter-data-center applications. Optimizing the EAM-DFBLD transmitter at 1306.8 nm with a bias current of 100 mA and a bias voltage of -1.5 V can provide a 34.5-GHz bandwidth for 16-QAM discrete multitone (DMT) and GFDM formats. The parametric comparison reveals that the 16-QAM GFDM with K = 16 can improve its performance from BtB to 10-km SMF transmission with decoding BER slightly increasing from 1.44 x 10(-3) to 2.82 x 10(-3). The EAM-DFBLD carried GFDM data also provides a better power penalty by -0.25 dB. This enables higher raw data rates of 149.6 Gbit/s under BtB case and 138 Gbit/s in 10-km SMF transmission link below the forward error correction (FEC) criterion. The GFDM format carried by the EAM-DFBLD transmitter thus becomes the more suitable candidate than the DMT to implement ultra-high-speed inter-data-center and data access link applications.

关键词： Bandwidth Modulation Optical transmitters Symbols Quadrature amplitude modulation Adaptive optics Time-frequency analysis machine learning algorithm electro-absorption modulator distributed feedback laser diode quadrature amplitude modulation discrete multitone generalized frequency division multiplexing inter-data-center

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Performance optimization of cement calciner based on CFD simulation and machine learning algorithm

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ENERGY 2024年 302卷

作者： Cui, Ying Ye, Lin Yao, Zhongran Gu, Xiaoyong Wang, Xinwang Wuxi Inst Technol Sch Automot & Transportat Wuxi 214000 Jiangsu Peoples R China Southeast Univ Sch Energy & Environm Key Lab energy Convers & Proc Measurement & Contro Minist Educ Nanjing 210096 Jiangsu Peoples R China Monash Univ Dept Chem Engn ARC Res Hub Computat Particle Technol Clayton Vic 3800 Australia Wuxi Inst Technol Sch Control Wuxi 214000 Jiangsu Peoples R China Gaolang West Rd 1600 Wuxi 214000 Jiangsu Peoples R China

In order to improve the combustion efficiency and decomposition rate of the cement calciner and reduce pollutant emission, a performance optimization method based on Computational Fluid Dynamics (CFD) numerical simulation integrated with machine learning is proposed. The Multiphase Particle-in-cell (MP-PIC) method and the chemical reaction models are employed to simulate the coal combustion and CaCO3 decomposition process, whose calculation results are combined with the industrial practical data of the cement plant, so as to establish a more comprehensive training database. On this basis, a novel Topology Particle Swarm Optimization algorithm integrating with Convolutional Neural Network and Long Short-Term Memory (RITPSO-CNN-LSTM) algorithm model is established to predict combustion efficiency, decomposition rate, and NOx emission, respectively. Results show that compared with two other relative basic algorithm models, the prediction error of the proposed algorithm model for the three targets is minimal with the average relative error of 0.045 %, 0.038 %, and 0.021 %, respectively. The addition of CFD simulation data makes the prediction model more applicable with higher stability and accuracy. Based on the prediction results, Grey Wolf Optimizer (GWO) algorithm is employed to optimize operating parameters, and finally the average optimization amount of combustion efficiency, decomposition rate, and NOx emission are 2.17 %, 2.24 %, and 6.15 ppm, respectively, which meet the optimization requirements.

关键词： Cement calciner Combustion characteristics Calcium carbonate decomposition MP-PIC simulation machine learning algorithm

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Retrieval of Water Quality Parameters Based on Near-Surface Remote Sensing and machine learning algorithm

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REMOTE SENSING 2022年第21期14卷 5305-5305页

作者： Zhao, Yubo Yu, Tao Hu, Bingliang Zhang, Zhoufeng Liu, Yuyang Liu, Xiao Liu, Hong Liu, Jiacheng Wang, Xueji Song, Shuyao Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Peoples R China Chinese Acad Sci Key Lab Spectral Imaging Technol Xian 710119 Peoples R China Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710119 Peoples R China Univ Chinese Acad Sci Sch Optoelect Beijing 100049 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

With the development of industrialization and urbanization, the consumption and pollution of water resources are becoming more and more serious. Water quality monitoring is an extremely important technical means to protect water resources. However, the current popular water quality monitoring methods have their shortcomings, such as a low signal-to-noise ratio of satellites, poor time continuity of unmanned aerial vehicles, and frequent maintenance of in situ underwater probes. A non-contact near-surface system that can continuously monitor water quality fluctuation is urgently needed. This study proposes an automatic near-surface water quality monitoring system, which can complete the physical equipment construction, data collection, and processing of the application scenario, prove the feasibility of the self-developed equipment and methods and obtain high-performance retrieval results of four water quality parameters, namely chemical oxygen demand (COD), turbidity, ammoniacal nitrogen (NH3-N), and dissolved oxygen (DO). For each water quality parameter, fourteen machine learning algorithms were compared and evaluated with five assessment indexes. Because the ensemble learning models combine the prediction results of multiple basic learners, they have higher robustness in the prediction of water quality parameters. The optimal determination coefficients (R-2) of COD, turbidity, NH3-N, and DO in the test dataset are 0.92, 0.98, 0.95, and 0.91, respectively. The results show the superiority of near-surface remote sensing, which has potential application value in inland, coastal, and various water bodies in the future.

关键词： water quality monitoring near-surface remote sensing machine learning algorithm ensemble learning model

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Integrating machine learning algorithm with sewer process model to realize swift prediction and real-time control of H2S pollution in sewer systems

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WATER RESEARCH X 2024年 23卷 100230页

作者： Liang, Zhensheng Xie, Wenlang Li, Hao Li, Yu Jiang, Feng Sun Yat Sen Univ Sch Environm Sci & Engn Guangdong Prov Key Lab Environm Pollut Control & R Guangzhou 510275 Peoples R China Sun Yat Sen Univ Guangdong Prov Int Joint Res Ctr Urban Water Manag Guangzhou 510275 Peoples R China South China Normal Univ Sch Environm Guangdong Prov Key Lab Chem Pollut & Environm Safe Guangzhou 510006 Peoples R China South China Normal Univ MOE Key Lab Theoret Chem Environm Guangzhou 510006 Peoples R China

The frequent occurrence of safety incidents in sewer systems due to the emergency toxicity of hydrogen sulfide (H2S) necessitate timely and efficient prediction, early warning and real-time control. However, various factors influencing H2S generation and emission leads to a substantial computational burden for the existing dynamic sewer process models and fails to timely control the H2S exposure risk. The present study proposed a swift prediction model (SPM) that combined the validated dynamic sewer process model (the biofilm-initiated sewer process model, BISM) with a high-speed machine learning algorithm (MLA), achieving accurately and swiftly predict the dissolved sulfide (DS) concentration and H2S concentration in a specific sewer network. Based on Gradient Boosting Decision Tree -based SPM, the simulated concentrations of DS and H2S are 1.95 mg S/L and 214 ppm, respectively, which are closely to the field -measured values of 1.82 mg S/L and 219 ppm. Notably, SPM achieved a computation time of less than 0.3 s, and a significant improvement over BISM ( > 5000 s) for the same task. Moreover, the real-time and dynamic dosing scheme facilitated by SPM outperformed the conventional constant dosing scheme provided by dynamic sewer process model, which significantly improved the H2S control completion rate from 69 % to 100 %, and achieved a significant reduction in chemical dosage. In conclusion, the integration of dynamic sewer process models with MLA addresses the inadequacy of monitoring data for MLA training, and thus pursues swift prediction of H2S generation and emission, and achieving real-time, effective, and economic control of H2S in complex sewer networks.

关键词： Sulfide pollution in sewers Validated dynamic sewer process model machine learning algorithm Swift prediction model Real-time control

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Application of random-forest machine learning algorithm for mineral predictive mapping of Fe-Mn crusts in the World Ocean

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ORE GEOLOGY REVIEWS 2023年 162卷

作者： Josso, Pierre Hall, Alex Williams, Christopher Le Bas, Tim Lusty, Paul Murton, Bramley British Geol Survey Environm Sci Ctr Nottingham NG12 5GG England Natl Oceanog Ctr Waterfront CampusEuropean Way Southampton SO14 3ZH England

Mineral prospectivity mapping constitutes an efficient tool for delineating areas of highest interest to guide future exploration. Multiple knowledge-driven approaches have been applied for the creation of prospectivity maps for deep-sea ferromanganese (Fe-Mn) crusts over the last decades. The results of a data-driven approach making use of an extensive data collection exercise on occurrences of Fe-Mn crusts in the World Ocean and recent increase in global marine datasets are presented. A Random Forest machine learning algorithm is applied, and results compared with previously established expert-driven maps. Optimal predictive conditions for the algorithm are observed for (i) a forest size superior to a hundred trees, (ii) a training dataset larger than 10%, and (iii) a number of predictors to be used as nodes superior to two. The confusion matrix and out-of-bag errors on the remaining unused data highlight excellent predictive capabilities of the trained model with a prediction accuracy for Fe-Mn crusts of 87.2% and 98.2% for non-crusts locations, with a Kohen's K index of 0.84, validating its application for prediction at the World scale. The slope of the seafloor, sediment thickness, sediment type, biological productivity, and abyssal mountain constitute the five strongest explanatory variables in predicting the occurrence of Fe-Mn crusts. Most 'hand-drawn' knowledge-driven prospective areas are also considered prospective by the random forest algorithm with notable exceptions along the coast of the American continent. However, poor correlation is observed with knowledge-driven GIS-based criterion mapping as the Random Forest considers un-prospective most target areas from the GIS approach. Overall, the Random Forest prediction performs better in predicting a high chance of Fe-Mn crust occurrence in ISA licensed area than the GIS approach, which constitutes an external validation of the predictive quality of the random forest model.

关键词： Ferromanganese crust Deep-sea mineral Prospectivity analysis Mineral resources machine learning algorithm Random forest

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