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A Raman spectral database of naturally aged plastics: A proof-of-concept study for waste plastic sorting

JOURNAL OF RAMAN SPECTROSCOPY 2023年第3期54卷 305-313页

作者： Marica, Ioana Pinzaru, Simona Cinta Babes Bolyai Univ Fac Phys Kogalniceanu 1 RO-400084 Cluj Napoca Romania

A Raman spectral database of naturally aged plastics for years is developed aiming to support efficient and proper plastic waste management based on Raman sorting technology. The database is designed as a website and contains the Raman spectral data for 146 plastic waste samples degraded for years in natural environmental conditions. Plastic samples aged for years in indoor conditions are also included along with a few undegraded, common plastics. The natural environmental degradation occurring for years of exposure to sun radiation, terrestrial and aquatic factors, or temperature variations is poorly understood in terms of spectral changes of representative plastics. The samples of choice are represented by the widespread types of plastics such as polyethylene terephthalate, high-density polyethylene, low-density polyethylene, polyvinyl chloride, polypropylene, and polystyrene. Each plastic sample is described individually on the database's website, and its Raman spectrum is available as a txt file for both educational and analytical purposes. Moreover, the database was validated by external validation of the machine-learning classifiers: k-nearest neighbor, support vector machine, logistic regression, random forest, and Naive Bayes resulting in a classification accuracy between 0.902 and 0.973.

关键词： machine-learning algorithm plastic Raman database plastic waste Raman sorting technology

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Large-signal behavior modeling of GaN HEMTs using SSA augmented ELM algorithm

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2023年第5期22卷 1415-1422页

作者： Wang, Shaowei Zhang, Jincan Yang, Shi Jin, Hao Xu, Binrui Wang, Jinchan Zhang, Liwen Henan Univ Sci & Technol Elect Engn Coll Luoyang 471023 Peoples R China Novaco Microelect Technol Ltd Nantong 226000 Peoples R China Luoyang Elect Equipment Test Ctr China Luoyang 471003 Peoples R China

The machine-learning algorithm is a technology that can learn from data. It can solve some problems that are difficult for humans to design and use deterministic programs. Among the available machine-learning algorithms for solving these problems, the Extreme learning machine (ELM) algorithm is well known because it only needs to set the number of the network hidden layer nodes and could generate a unique optimal solution. And, due to the randomness of its connection weights and thresholds, the simulated effect is random. Therefore, the selection of weights and thresholds needs to be optimized. In this paper, an enhanced ELM model is proposed to model the large-signal characteristics of GaN High Electron Mobility Transistors (HEMTs). The Sparrow Search algorithm (SSA) is used to optimize the initial weights and thresholds of the ELM algorithm, which significantly improves the prediction ability. Moreover, the SSA algorithm has the characteristics of strong optimization ability and fast convergence speed, which greatly improves the feasibility of model training. Through comparing the training effects of the SSA-ELM model and the ELM model, it can be seen that the proposed SSA-ELM model improves the ability of the ELM model to simulate the large-signal characteristics of GaN HEMTs.

关键词： machine-learning algorithm ELM model SSA-ELM model GaN HEMT

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Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithms

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COMPUTERS & FLUIDS 2022年 246卷

作者： Zhang, Zexia Limaye, Ajay B. Khosronejad, Ali SUNY Stony Brook Civil Engn Dept Stony Brook NY 11794 USA Univ Virginia Dept Environm Sci Charlottesville VA 22904 USA

machine learning methods have been extensively used to study the dynamics of complex fluid flows. One such algorithm, known as adaptive neural fuzzy inference system (ANFIS), can generate data-driven predictions for flow fields, but has not been applied to natural geophysical flows in large-scale rivers. Herein, we demonstrate the potential of ANFIS to produce three-dimensional (3D) realizations of the instantaneous flood flow field in several large-scale, virtual meandering rivers. The 3D dynamics of flood flow in large-scale rivers were obtained using large-eddy simulation (LES). The LES results, i.e., the 3D velocity components, were employed to train the learnable coefficients of an ANFIS. The trained ANFIS, along with a few time-steps of LES results (precursor data) were then used to produce 3D realizations of flood flow fields in large-scale rivers with geometries other than the one the ANFIS was trained with. We also used the trained ANFIS to generate 3D realizations of river flow at a discharge other than that the ANFIS was trained with. The flow field results obtained from ANFIS were validated using separate LES runs to assess the accuracy of the 3D instantaneous realizations of the machine learning algorithm. An error analysis was conducted to quantify the discrepancies among the ANFIS and LES results for various flood flow predictions in large-scale rivers.

关键词： machine-learning algorithm Large-scale rivers Flood flow predictions Large-eddy simulation

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Compared Insights on machine-learning Anomaly Detection for Process Control Feature

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Computers, Materials & Continua 2022年第11期73卷 4033-4049页

作者： Ming Wan Quanliang Li Jiangyuan Yao Yan Song Yang Liu Yuxin Wan School of Information Liaoning UniversityShenyang110036China School of Computer Science and Technology Hainan UniversityHaikou570228China School of Physics Liaoning UniversityShenyang110036China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016China Department of Electrical Engineering and Information Technology Karlsruhe Institute of TechnologyKarlsruhe76131Germany

Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber ***,different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature *** a sequence,after developing one feature generation approach,the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning *** on process control features generated by directed function transition diagrams,this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their matching ***,this paper not only describes some qualitative properties to compare their advantages and disadvantages,but also gives an in-depth and meticulous research on their detection accuracies and consuming *** the verified experiments,two attack models and four different attack intensities are defined to facilitate all quantitative comparisons,and the impacts of detection accuracy caused by the feature parameter are also comparatively *** experimental results can clearly explain that SVM(Support Vector machine)and WNN(Wavelet Neural Network)are suggested as two applicable detection engines under differing cases.

关键词： Anomaly detection machine-learning algorithm process control feature qualitative and quantitative comparisons

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Model-based prediction of the potential geographical distribution of the invasive coconut mite, Aceria guerreronis Keifer (Acari: Eriophyidae) based on MaxEnt

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AGRICULTURAL AND FOREST ENTOMOLOGY 2022年第3期24卷 390-404页

作者： Aidoo, Owusu Fordjour da Silva, Ricardo Siqueira Santana Junior, Paulo Antonio Corcino Souza, Philipe Guilherme Kyerematen, Rosina Owusu-Bremang, Felix Yankey, Ndede Borgemeister, Christian Univ Environm & Sustainable Dev UESD Sch Nat & Environm Sci Dept Biol Phys & Math Sci Somanya Ghana Univ Educ UEW Inst Teacher Educ & Continuing Profess Dev ITECPD Winneba Ghana Univ Fed Vales do Jequitinhonha & Mucuri UFVJM Dept Agron Diamantina Brazil Univ Fed Vicosa Dept Entomol Vicosa MG Brazil Univ Ghana Dept Anim Biol & Conservat Sci DABCS Legon Ghana Council Sci & Ind Res CSIR Oil Palm Res Inst Coconut Res Programme Sekondi Ghana Univ Bonn Ctr Dev Res ZEF Bonn Germany

The coconut mite Aceria guerreronis Keifer (Acari: Eriophyidae), is a destructive mite pest of coconut, causing significant economic losses. However, an effective pest management strategy requires a clear understanding of the geographical areas at risk of the target pest. Therefore, we predicted the potential global distribution A. guerreronis using a machine learning algorithm based on maximum entropy. The potential future distribution for A. guerreronis covered the 2040 and 2060 periods under two climate change emission scenarios (SSP1-2.6 and SSP5-5.85) in the context of the sixth assessment report (AR6) of the Intergovernmental Panel on Climate Change. The MaxEnt model predicts the habitat suitability for A. guerreronis outside its present distribution, with suitable habitats in Oceania, Asia, Africa, and the Americas. The habitat suitability for the pest will decrease from 2040 to 2060. The areas with the highest risk of A. guerreronis are those with an annual average temperature of around 25 degrees C, mean annual precipitation of about 1459 mm, mean precipitation seasonality close to 64%, an average variation of daytime temperature of about 8.6 degrees C, and mean seasonality of temperature of about 149.7 degrees C. Our findings provide information for quarantine measures and policymaking, especially where A. guerreronis is presently still absent.

关键词： Aceria guerreronis climate change coconut mite machine-learning algorithm MaxEnt

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Raman spectroscopy of follicular fluid and plasma with machine-learning algorithms for polycystic ovary syndrome screening

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MOLECULAR AND CELLULAR ENDOCRINOLOGY 2021年 523卷 111139-111139页

作者： Zhang, Xinyi Liang, Bo Zhang, Jun Hao, Xinyao Xu, Xiaoyan Chang, Hsun-Ming Leung, Peter C. K. Tan, Jichun China Med Univ Ctr Reprod Med Dept Obstet & Gynecol Shengjing Hosp Shenyang Liaoning Peoples R China Key Lab Reprod Dysfunct Dis & Fertil Remodeling L Shenyang Liaoning Peoples R China Shanghai Jiao Tong Univ Sch Life Sci & Biotechnol Joint Int Res Lab Metab & Dev Sci State Key Lab Microbial Metab Shanghai Peoples R China Basecare Med Device Co Suzhou Jiangsu Peoples R China Univ British Columbia BC Childrens Hosp Res Inst Dept Obstet & Gynaecol Room 317950 West 28th Ave Vancouver BC V5Z 4H4 Canada

Polycystic ovary syndrome (PCOS) is the main cause of anovulatory infertility and affects women throughout their lives. The specific diagnostic method is still under investigation. In the present study, we aimed to identify the metabolic tracks of the follicular fluid and plasma samples from women with PCOS by performing Raman spectroscopy with principal component analysis and spectral classification models. Follicular fluid and plasma samples obtained from 50 healthy (non-PCOS) and 50 PCOS women were collected and measured by Raman spectroscopy. Multivariate statistical methods and different machine-learning algorithms based on the Raman spectra were established to analyze the results. The principal component analysis of the Raman spectra showed differences in the follicular fluid between the non-PCOS and PCOS groups. The stacking classification models based on the k-nearest-neighbor, random forests and extreme gradient boosting algorithms yielded a higher accuracy of 89.32% by using follicular fluid than the accuracy of 74.78% obtained with plasma samples in classifying the spectra from the two groups. In this regard, PCOS may lead to the changes of metabolic profiles that can be detected by Raman spectroscopy. As a novel, rapid and affordable method, Raman spectroscopy combined with advanced machine-learning algorithms have potential to analyze and characterize patients with PCOS.

关键词： Polycystic ovary syndrome Raman spectroscopy machine-learning algorithm Follicular fluid Screening

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Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 128卷

作者： Asgarkhani, N. Kazemi, F. Jakubczyk-Galczynska, A. Mohebi, B. Jankowski, R. Gdansk Univ Technol Fac Civil & Environm Engn Ul Narutowicza 11-12 PL-80233 Gdansk Poland UCL Dept Civil Environm & Geomat Engn London England Imam Khomeini Int Univ Fac Engn & Technol Qazvin Iran

Nowadays, Buckling-Restrained Brace Frames (BRBFs) have been used as lateral force-resisting systems for low-, to mid-rise buildings. Residual Interstory Drift (RID) of BRBFs plays a key role in deciding to retrofit buildings after seismic excitation;however, existing formulas have limitations and cannot effectively help civil engineers, e.g., FEMA P-58, which is a conservative estimation method. Therefore, there is a need to provide a comprehensive tool for estimating seismic responses of Interstory Drift (ID) and RID with novel approaches to fulfill the shortcomings of existing formulas. The machine learning (ML) method is an interdisciplinary approach that makes it possible to solve these types of engineering problems. Therefore, the current study proposes ML algorithms to provide a prediction model for determining the seismic response, seismic performance curve, and seismic failure probability curve of BRBFs. To train ML-based prediction models, Nonlinear Time-History Analysis (NTHA) and Incremental Dynamic Analysis (IDA) were performed on the 2-, to 12-Story BRBFs subjected to 78 far-field ground motions, and 606944 data points were prepared for different prediction purposes. The results indicate that the considered approaches are justified. For instance, the proposed ML methods have the ability to predict the maximum ID, maximum RID and maximum roof ID with the accuracy of even 98.7%, 95.2%, and 93.8%, respectively, for the 4-Story BRBF. Moreover, a general preliminary estimation tool is introduced to provide predictions based on the input parameters considered in the study.

关键词： Buckling-restrained braced frame machine-learning algorithm Residual interstory drift Seismic retrofit Seismic performance curve Seismic failure probability

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Early identification of sepsis in burn patients using compensatory reserve measurement: A prospective case series study

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Burns Open 2022年第4期6卷 137-145页

作者： Convertino, Victor A. Wagner, Amanda R. Akers, Kevin S. VanFosson, Christopher A. Cancio, Leopoldo C. US Army Institute of Surgical Research JBSA Fort Sam Houston TX United States

Background: Early identification of sepsis can be lifesaving, but current diagnostic approaches rely on standard vital signs and biochemistry analyses that delay recognition. We postulate that early identification of sepsis could be accomplished with a clinical assessment of real-time changes in physiological responses that reflect compensation to relative hypovolemia, or the compensatory reserve measurement (CRM). In this pilot study, we measured CRM in burn patients who transitioned from a non-infected state to sepsis to determine if CRM would be reduced prior to clinical identification of sepsis using traditional standard-of-care criteria. Methods: Each subject underwent placement of a radial arterial catheter upon arrival to the burn ICU. Morphology of analog arterial waveforms was recorded and analyzed based on a machine-learning algorithm, employing feature-extraction techniques that provided CRM values on a relative scale of 100% (normal) to 0% (onset of decompensated shock). CRM values one (D-1) or two (D-2) times prior to the day of sepsis diagnosis (D+0) were compared in time to standard diagnostic techniques. Results: CRM was lower in all patients on D+0 compared to ICU admission day. Complete analyzable arterial waveform data collected on D-1 and D-2 were captured from 5 subjects. These patients demonstrated an average reduction (p = 0.003) in CRM from 48 ± 14% on D-2 to 31 ± 14% on D-1 prior to sepsis diagnosis with standard criteria on D+0. Conclusion: The results of this clinical investigation conducted in burn patients provide the first data to substantiate a capability for early diagnosis of sepsis by measurement of the compensatory reserve. Our findings support the notion that this novel technology could offer caregivers of at-risk burn patients with a user-friendly decision-support indicator of infection for individualized triage and treatment. Level of evidence: Diagnostic, level IV. © 2022

关键词： Burns Infection machine-learning algorithm Monitoring Sepsis

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Hypoxia Detection for Confined-Space Workers: Photoplethysmography and machine-learning Techniques

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SN Computer Science 2022年第4期3卷 290页

作者： Wei, Yixuan Jin, Longzhe Wang, Shu Xu, Yifei Ding, Tianqi School of Civil Engineering and Resources University of Science and Technology Beijing Beijing 100083 China

Oxygen-deficiency is a cause of fatalities in confined-space workplaces. Most research and projects have been conducted to reduce work-related accidents by external measurements, while works addressing the early warning of workers’ hypoxia state through a bio-electrical signal have rarely been conducted. In this paper, we present a hypoxia detection system based on non-invasive photoplethysmograph (PPG) measurement using machine-learning (ML) algorithms. The PPG signals obtained from 22 subjects underwent preprocessing and features extraction steps. Time-domain features, rising and falling slopes, and amplitude parameters were adopted to train and test the ML algorithms. In addition, an Internet of Things- (IoT) based smart wearable device and monitoring system were developed to measure the vital parameters of workers in confined spaces. Cardiac cycle time of all the participants except subject 8 decreased significantly (P < 0.01) throughout the oxygen-deficiency trial. The PPG signal complexity essentially decreased (P = 0.006) when the concentration of environmental oxygen declined. The DT algorithm with nine input parameters outperformed the other algorithms in prediction accuracy (Acc = 0.9). The results show that the features extracted from the PPG signal can be adopted as important indicators to revealing the hypoxia state of workers. The device and system automatically measure and analyze the PPG signal with the location details of caregivers and notify the monitoring staff in the case of an emergency. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Confined space Hypoxia Internet of Things (IoT) machine-learning algorithm Photoplethysmography (PPG)

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Load Management of a Benchmark Electric Vehicle Charger Using an Intelligent Controller for HIL Implementation 7

Load Management of a Benchmark Electric Vehicle Charger Usin...

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IEEE 7th Southern Power Electronics Conference (SPEC)

作者： Nademi, Hamed Calif State Univ Dept Elect Engn San Marcos CA 92096 USA

ISBN: (纸本)9798350399882

An intelligent controller is developed based on machine learning algorithm incorporated into predictive control strategy. The model of interest is trained during transfer learning process utilizing measurement dataset. The introduced control scheme provides an optimal load dispatching among distributed energy resources (DERs) interconnected with a benchmarked Electric Vehicle (EV) battery charger. The proposed scheme offers an easy realization on various embedded hardware systems independently. In this study, a few operational conditions of an exemplary charging station are examined via Hardware-in-the-Loop (HIL) testing. The findings are then compared to the classical finite-control-set model predictive control (FCS-MPC) which has been widely used in recent studies. Several operation scenarios have been evaluated and the obtained results are paving the way for future solutions addressing the concern of utilities pertaining to DERs load management for vehicle-to-grid (V2G) technologies.

关键词： electric vehicle hardware-in-the-loop testing machine-learning algorithm power converters predictive control

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