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A smart walker based on a hybrid motion model and machine learning method

MECHATRONICS 2023年 96卷

作者： Cheng, Jinsai Shen, Tao Kent State Univ Coll Aeronaut & Engn 1400 Lefton Esplanade Kent OH 44242 USA

Due to the degeneration of musculoskeletal structure and strength, the elderly population is facing significant mobility challenges. Walkers are widely used for mobility-impaired people. In this research, a smart walker has been developed by using a hybrid motion model (HMM) and a machine learning model based on the vertical interaction force to match the velocity between the walker and its user, which can reduce the horizontal interaction force thus to help lower the operational effort. It is equipped with only force sensors on the handlebars as a human-robot interface. The human motion is modeled by using the HMM in which an inverted pendulum model is used in the single support phase and a constant velocity model is used in the double support phase. A novel gait phase detector is built based on a machine learning method to correlate the force information and the walking gait phases. Experiments with three subjects are carried out to verify the effectiveness of the gait phase detection method and the hybrid motion model. The results demonstrate that the walker can accurately identify gait phases with more than 90 % accuracy and the horizontal interaction force is reduced to around 50 % of that under the constant velocity model and power-off condition when using the HMM during walking.

关键词： Human robot interaction Hybrid human motion model machine learning algorithm Smart walker

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Driver Drowsiness Detection Using machine learning to Prevent Accidents 2nd

Driver Drowsiness Detection Using Machine Learning to Preven...

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2nd Conference of Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2020

作者： Sriramulu, Srinivasan Daniel, A. Partheeban, N. Singh, Vaibhav Khan, Asad Ali Sharma, Shivam School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789811902956

Drowsiness of driver is one of the major reasons for Accidents. Every year the number of such cases increases. In this paper, A drowsiness detector is presented that will reduce the number of such cases and hence increase the transportation safety. The system works on machine learning algorithm that locates the driver’s face and eyes and closely monitors eye blink pattern of driver and alarms him in case of drowsiness. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blink pattern Driver drowsiness machine learning algorithm Transportation safety

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Thermal Images Based Mobile Application for Identifying Faults in Photovoltaics Modules

Thermal Images Based Mobile Application for Identifying Faul...

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International Symposium on Fundamentals of Electrical Engineering (ISFEE)

作者： Constantin, Alexandru-Ionel Banaseanu, Ramona-Nicoleta Anghelita, Paula Iosif, Gabriela Marin, Dorian Natl Inst Res & Dev Elect Engn ICPE CA Dept Renewable Resources & Energy Efficiency Bucharest Romania Natl Univ Sci & Technol POLITEHN Bucharest Fac Automat Control & Comp Sci Bucharest Romania Natl Univ Sci & Technol POLITEHN Bucharest Fac Elect Telecommun & Informat Technol Bucharest Romania Natl Inst Res & Dev Elect Engn ICPE CA Off Management Knowledge & Informat Bucharest Romania

ISBN: (纸本)9798350308570;9798350308563

The paper describes few faults that can be present in Photovoltaic modules and presents results related to training and the testing of a mobile application designed for identifying faults in photovoltaic modules based on thermal images. The mobile application was trained and then tested with different individual and multiple faults. The mobile application can identify with high accuracy both the individual and multiple faults if the thermal images are preprocessed before to be uploaded for analysis.

关键词： machine learning algorithm mobile application photovoltaic module fault detection hot spot bypass diode junction box

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Predicting the weld width from high-speed successive images of the weld zone using different machine learning algorithms during laser welding

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2019年第5期16卷 5595-5612页

作者： Cai, Wang Wang, Jianzhuang Cao, Longchao Mi, Gaoyang Shu, Leshi Zhou, Qi Jiang, Ping Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Mat Sci & Engn Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Hubei Peoples R China

The dynamic behavior of the keyhole and molten pool is associated with the quality of weld seam. In this study, an on-line visual monitoring system is devised to photograph the keyhole and molten pool during external magnetic field assisted laser welding on the AISI 2205 duplex stainless steel plates. Seven features are defined to describe the morphology of the keyhole and molten pool. Then, the principal component analysis (PCA) algorithm is applied to reduce the dimensions of these features to obtain different number of principal components (PCs). Three different machine learning algorithms, i.e. the back propagation neural network (BPNN), the radial based function neural network (RBFNN) and the support vector regression (SVR), are utilized to fit the relationship between the chosen PCs and the weld width. Finally, the global and local prediction accuracy of these three machine learning algorithms are compared under different number of PCs. Results illustrated that data dimensionality reduction is helpful to improve the modeling efficiency. machine learning algorithms can be exploited to predict the weld quality during laser welding with high accuracy. Among them, the BPNN model performs best and SVR model performs better than RBFNN model in this research. This work aims to model the relation between the features in weld zone and the weld quality with different machine learning algorithms, and provides a guideline of model selection for laser welding on-line monitoring and a necessary foundation for realizing intelligent welding with advanced algorithm.

关键词： laser welding keyhole molten pool image processing machine learning algorithm weld width

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Systematic review of machine learning models for personalised dosing of heparin

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BRITISH JOURNAL OF CLINICAL PHARMACOLOGY 2021年第11期87卷 4124-4139页

作者： Falconer, Nazanin Abdel-Hafez, Ahmad Scott, Ian A. Marxen, Sven Canaris, Stephen Barras, Michael Princess Alexandra Hosp Dept Pharm Brisbane Qld 4102 Australia Univ Queensland Sch Pharm Brisbane Qld 4102 Australia Univ Queensland Princess Alexandra Hosp Ctr Hlth Serv Res Level TwoBldg 33 Brisbane Qld 4102 Australia Princess Alexandra Hosp Clin Informat Brisbane Qld 4102 Australia Princess Alexandra Hosp Dept Internal Med & Clin Epidemiol Brisbane Qld Australia Univ Queensland Fac Med Sch Clin Med Brisbane Qld 4102 Australia Logan & Beaudesert Hosp Metro South Hlth Dept Pharm Brisbane Qld 4131 Australia

Aim To identify and critically appraise studies of prediction models, developed using machine learning (ML) methods, for determining the optimal dosing of unfractionated heparin (UFH). Methods Embase, PubMed, CINAHL, Web of Science, International Pharmaceutical Abstracts and IEEE Xplore databases were searched from inception to 31 January 2020 to identify relevant studies using key search terms synonymous with artificial intelligence or ML, 'prediction', 'dose', 'activated partial thromboplastin time (aPTT)' and 'UFH.' Studies had to have used ML methods for developing models that predicted optimal dose of UFH or target therapeutic aPTT levels in the hospital setting. The CHARMS Checklist was used to assess quality and risk of bias of included studies. Results Of 8393 retrieved abstracts, 61 underwent full text review and eight studies met inclusion criteria. Four studies described models for predicting aPTT, three studies described models predicting optimal dose of heparin during dialysis and one study described a model that used surrogate outcomes of clotting and bleeding to predict a therapeutic aPTT. Studies varied widely in reporting of study participants, feature characterisation and selection, handling of missing data, sample size calculations and the intended clinical application of the model. Only one study conducted an external validation and no studies evaluated model impacts in clinical practice. Conclusion Studies of ML models for UFH dosing are few and none report a model ready for routine clinical use. Existing studies are limited by low methodological quality, inadequate reporting of study factors and absence of external validation and impact analysis.

关键词： dose prediction machine learning algorithm predictive model UFH unfractionated heparin

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machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction

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COMPUTERS & STRUCTURES 2023年 274卷

作者： Kazemi, F. Jankowski, R. Gdansk Univ Technol Fac Civil & Environm Engn Ul Narutowicza 11-12 PL-80233 Gdansk Poland

Regarding the unpredictable and complex nature of seismic excitations, there is a need for vulnerability assessment of newly constructed or existing structures. Predicting the seismic limit-state capacity of steel Moment-Resisting Frames (MRFs) can help designers to have a preliminary estimation and improve their views about the seismic performance of the designed structure. This study improved data-driven decision techniques in Python software, known as supervised machine learning (ML) algorithms, to find median IDA curves (M-IDAs) for predicting the seismic limit-state capacities of steel MRFs considering Soil-Structure Interaction (SSI) effects. For this purpose, Incremental Dynamic Analyses (IDAs) were performed on the steel MRFs from two to nine-story elevations modeled in Opensees subjected to three ground motion subsets of Far Fault (FF), near-fault Pulse-Like (PL) and No-Pulse (NP) suggested by FEMA-P695. The result of the analysis confirmed that there is no specific model for predicting the MIDA curve of steel structures;therefore, the best developed ML algorithms to reduce a complex modeling process with high computational cost using 128,000 data points were proposed. To provide convenient access to prediction results, Graphical User Interface (GUI) was developed to predict Sa(T1) of seismic limit-state performance levels with a large database based on prediction models. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： machine learning algorithm Data -driven decision techniques Supervised learning Soil -structure interaction Seismic vulnerability assessment Seismic limit -state capacity

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Combining process-based model and machine learning to predict hydrological regimes in floodplain wetlands under climate change

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JOURNAL OF HYDROLOGY 2023年第PartA期626卷

作者： Yao, Siyang Chen, Cheng Chen, Qiuwen Zhang, Jianyun He, Mengnan Nanjing Hydraul Res Inst Natl Key Lab Water Disaster Prevent Nanjing 210029 Peoples R China Wuhan Univ State Key Lab Water Resources Engn & Management Wuhan 430072 Peoples R China Nanjing Hydraul Res Inst Ctr Ecoenvironm Res Nanjing 210029 Peoples R China Yangtze Inst Conservat & Green Dev Nanjing 210029 Peoples R China

Climate change has remarkable effects on the hydrological regime in floodplain wetlands. However, the hydrodynamic simulation under climate change conditions suffers from the uncertainty of the downstream water level boundary. In this study, an effective approach was developed by integrating an advanced machine learning algorithm and a process-based watershed hydrological model to predict the lake level under climate change. The developed approach was validated in a vital conservation wetland in the Ramsar Convention, the Poyang Lake floodplain wetland in China and utilized to simulate the hydrological regime in climate change. We found that the Long Short-term Memory (LSTM) performed relatively well in the simulation of water level, with coefficients of determination (R2) of 0.94 and 0.83 in the training and testing periods, respectively. The mean cumulative flooding time in the future period was slightly less than that in the baseline period, with a decreased range of 1.01 -12.05 days/year. The highest and mean water levels rose significantly in the wet season, with a risen range of 1.01 -3.3 and 1.56 -2.44 m, respectively, and the appearance of extreme flood events became more frequent. The maximum, minimum, and mean inflows for Poyang Lake in the dry season exhibited a remarkable reduction trend, especially for the minimum inflow, with a reduced range of 59.37%-79.22%, indicating that more serious extreme drought events would appear in Poyang Lake. The developed approach has good performance for water level simulation and extreme runoff prediction, thus providing a viable way for future water resources management in floodplain wetlands.

关键词： Hydrological model machine learning algorithm Long short-term memory Hydrological regime Climate change

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machine learning-based 3D segmentation of mitochondria in polarized epithelial cells

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MITOCHONDRION 2024年 76卷 101882页

作者： Hultgren, Nan W. Zhou, Tianli Williams, David S. Univ Calif Los Angeles Dept Ophthalmol Los Angeles CA 90095 USA Univ Calif Los Angeles Stein Eye Inst Los Angeles CA 90095 USA Univ Calif Los Angeles David Geffen Sch Med Dept Neurobiol Los Angeles CA 90095 USA Univ Calif Los Angeles Mol Biol Inst Los Angeles CA 90095 USA Univ Calif Los Angeles Brain Res Inst Los Angeles CA 90095 USA

Mitochondria are dynamic organelles that alter their morphological characteristics in response to functional needs. Therefore, mitochondrial morphology is an important indicator of mitochondrial function and cellular health. Reliable segmentation of mitochondrial networks in microscopy images is a crucial initial step for further quantitative evaluation of their morphology. However, 3D mitochondrial segmentation, especially in cells with complex network morphology, such as in highly polarized cells, remains challenging. To improve the quality of 3D segmentation of mitochondria in super-resolution microscopy images, we took a machine learning approach, using 3D Trainable Weka, an ImageJ plugin. We demonstrated that, compared with other commonly used methods, our approach segmented mitochondrial networks effectively, with improved accuracy in different polarized epithelial cell models, including differentiated human retinal pigment epithelial (RPE) cells. Furthermore, using several tools for quantitative analysis following segmentation, we revealed mitochondrial fragmentation in bafilomycin-treated RPE cells.

关键词： Mitochondrial morphology 3D segmentation machine learning algorithm Retinal pigment epithelium ImageJ

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Exploring the toxicological impact of bisphenol a exposure on psoriasis through network toxicology, machine learning, and multi-dimensional bioinformatics analysis

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025年 385卷 125708页

作者： Feng, Chun Yan, Wen Mei, Zhen Luo, Xin Zunyi Med Univ Affiliated Hosp Dept Dermatol Zunyi Peoples R China Emory Univ Sch Med Dept Neurol Atlanta GA USA Cent South Univ Xiangya Hosp 2 Clin Med Res Ctr Major Skin Dis & Skin Hlth Hunan Dept DermatolHunan Key Lab Med Epigen 139 Renmin Middle Rd Changsha Hunan Peoples R China

Psoriasis is a common immune - mediated skin disease, the pathogenesis of which is not completely elucidated. Environmental factors are key to its onset and progression. Bisphenol A (BPA) is a ubiquitous environmental pollutant that endangers human health. Previous research shows that BPA exposure disrupts immunity and causes skin inflammation and autoimmune diseases. However, the role and molecular mechanisms of BPA in psoriasis are unclear. In this study, we used network toxicology, machine learning, and bioinformatics to study BPA - induced psoriasis mechanisms. Public database analyses identified 100 potential targets, with significant enrichment in the PI3K - AKT and Chemokine signaling pathways. machine learning identified five core targets: PTAFR, MMP9, CXCR2, IDO1, and LCK. These genes are highly expressed in psoriatic lesion tissues than controls and associated to immune cell infiltration. Molecular docking and dynamics simulations confirmed stable interactions between BPA and these targets, which supports their role in disease progression. We also developed a novel Adverse Outcome Pathway (AOP) framework for BPA-induced psoriasis, providing key toxicological insights into the risks of exposure. These findings highlight the impact of BPA on immune regulation, offering a foundation for understanding associated health risks and formulating mitigation strategies. Our study provides an in-depth exploration of the molecular mechanisms underlying BPA-induced psoriasis. The findings underscore the practical application of integrating network toxicology, machine learning, multidimensional bioinformatics approaches, and AOP frameworks in assessing environmental pollutant risks. Furthermore, it lays the foundation for understanding BPA-related health risks and developing strategies to mitigate its impact on psorasis.

关键词： Psoriasis Network toxicology Molecular docking machine learning algorithm Bisphenol A Molecular dynamics simulation

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Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification

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HEALTHCARE TECHNOLOGY LETTERS 2021年第6期8卷 139-147页

作者： Panchavati, Saarang Lam, Carson Zelin, Nicole S. Pellegrini, Emily Barnes, Gina Hoffman, Jana Garikipati, Anurag Calvert, Jacob Mao, Qingqing Das, Ritankar Dascena Inc Div Data Sci 12333 Sowden Rd St B PMB 65148 Houston TX 77080 USA

Diagnosis and appropriate intervention for myocardial infarction (MI) are time-sensitive but rely on clinical measures that can be progressive and initially inconclusive, underscoring the need for an accurate and early predictor of MI to support diagnostic and clinical management decisions. The objective of this study was to develop a machine learning algorithm (MLA) to predict MI diagnosis based on electronic health record data (EHR) readily available during Emergency Department assessment. An MLA was developed using retrospective patient data. The MLA used patient data as they became available in the first 3 h of care to predict MI diagnosis (defined by International Classification of Diseases, 10th revision code) at any time during the encounter. The MLA obtained an area under the receiver operating characteristic curve of 0.87, sensitivity of 87% and specificity of 70%, outperforming the comparator scoring systems TIMI and GRACE on all metrics. An MLA can synthesize complex EHR data to serve as a clinically relevant risk stratification tool for MI.

关键词： Medical administration complex EHR data machine learning algorithm MI diagnosis retrospective patient data machine learning clinical decision support tool diseases diagnostic management decisions Biology and medical computing medical diagnostic computing clinically relevant risk stratification tool Decision support systems myocardial infarction risk stratification electronic health records Emergency Department assessment clinical management decisions health care retrospective validation patient diagnosis cardiovascular system electronic health record data time 3.0 hour learning (artificial intelligence) patient treatment MLA decision support systems clinical measures

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