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Hyperparameter Bayesian Optimization of Gaussian Process Regression Applied in Speed-Sensorless Predictive Torque Control of an Autonomous Wind Energy Conversion System

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ENERGIES 2023年第12期16卷 4738页

作者： Hamoudi, Yanis Amimeur, Hocine Aouzellag, Djamal Abdolrasol, Maher G. M. Ustun, Taha Selim Univ Bejaia Fac Technol Lab Maitrise Energies Renouvelables LMER Bejaia 06000 Algeria Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Malaysia Natl Inst Adv Ind Sci & Technol Fukushima Renewable Energy Inst Koriyama 9630298 Japan

This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). To achieve accurate estimation of non-linear quantities, the Gaussian Process Regression algorithm (GPR) is employed as a powerful machine learning tool for designing speed and flux estimators. To enhance the capabilities of the GPR, two improvements were implemented, (a) hyperparametric optimization through the Bayesian optimization (BO) algorithm and (b) curation of the input vector using the gray box concept, leveraging our existing knowledge of the ADSIG. Simulation results have demonstrated that the proposed GPR-PTC would remain robust and unaffected by the absence of a speed sensor, maintaining performance even under varying magnetizing inductance. This enables a reliable and cost-effective control solution.

关键词： predictive torque control supervised learning algorithm Gaussian Process Regression sensorless speed control hyperparameter Bayesian optimization

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Car Driver's Sympathetic Reaction Detection Through Electrodermal Activity and Electrocardiogram Measurements

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2020年第12期67卷 3413-3424页

作者： Zontone, Pamela Affanni, Antonio Bernardini, Riccardo Piras, Alessandro Rinaldo, Roberto Formaggia, Fabio Minen, Diego Minen, Michela Savorgnan, Carlo Univ Udine Dipartimento Politecn Ingn & Architetura Udine Italy VI Grade Srl I-33010 Tavagnacco Italy

Objective: in this paper we propose a system to detect a subject's sympathetic reaction, which is related to unexpected or challenging events during a car drive. Methods: we use the Electrocardiogram (ECG) signal and the Skin Potential Response (SPR) signal, which has several advantages with respect to other Electrodermal (EDA) signals. We record one SPR signal for each hand, and use an algorithm that, selecting the smoother signal, is able to remove motion artifacts. We extract statistical features from the ECG and SPR signals in order to classify signal segments and identify the presence or absence of emotional events via a supervised learning algorithm. The experiments were carried out in a company which specializes in driving simulator equipment, using a motorized platform and a driving simulator. Different subjects were tested with this setup, with different challenging events happening on predetermined locations on the track. Results: we obtain an Accuracy as high as 79.10% for signal blocks and as high as 91.27% for events. Conclusion: results demonstrate the good performance of the presented system in detecting sympathetic reactions, and the effectiveness of the motion artifact removal procedure. Significance: our work demonstrates the possibility to classify the emotional state of the driver, using the ECG and EDA signals and a slightly invasive setup. In particular, the proposed use of SPR and of the motion artifact removal procedure are crucial for the effectiveness of the system.

关键词： Stress Electrocardiography Skin Motion artifacts Biomedical measurement Feature extraction Support vector machines Stress detection skin potential response motion artifact electrocardiogram supervised learning algorithm

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Quality-Based Ranking of Translation Outputs

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IT PROFESSIONAL 2020年第4期22卷 21-27页

作者： Bharti, Nivedita Joshi, Nisheeth Mathur, Iti Katyayan, Pragya Banasthali Vidyapith Vanasthali India Banasthali Vidyapith Dept Comp Sci Vanasthali India

Translation ranking is inherently of great significance for machine translation (MT), as it allows the comparison of performances of multiple MT systems as well as for its efficient training. This article demonstrates a mechanism that is used for ranking the translation outputs generated by the MT systems from best to worst. To implement this approach, the system exploits a supervised learning algorithm trained over existing manual ranking by using various features obtained after the linguistic analysis of both source and target side sentences without relying on the reference translation.

关键词： Language Translation learning Artificial Intelligence Natural Language Processing Reference Translation supervised learning algorithm Multiple MT Systems Machine Translation Translation Ranking Quality Based Ranking Feature Extraction supervised learning Task Analysis Ranking Statistics Predictive Models Linguistics

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Prediction of heat transfer distribution induced by the variation in vertical location of circular cylinder on Rayleigh-Benard convection using artificial neural network

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2021年 209卷 106701-106701页

作者： Seo, Young Min Pandey, Sudhanshu Lee, Hyeon Uk Choi, Changyoung Park, Yong Gap Ha, Man Yeong Rolls Royce & Pusan Natl Univ Technol Ctr Busan South Korea Pusan Natl Univ Sch Mech Engn Busan South Korea Changwon Natl Univ Sch Mech Engn Chang Won South Korea LG Elect Home Appliance & Air Solut Co Chang Won South Korea

The present study investigates the flow and thermal fields of Rayleigh-Benard convection (RBC) in a rectangular channel with an internal circular cylinder. The parameters considered are Rayleigh number (10(4) <= Ra <= 10(6) ), Prandtl number (Pr = 0.7), and irreversibility distribution ratio (phi = 1). The vertical distance (delta) in the range of -0.2 <= delta <= 0.2 is the major simulation parameter in present study. The results are analyzed based on the iso-surface of temperature, vortical structure with orthogonal enstrophy distribution, and entropy generations. Additionally, Nusselt number ( Nu ) and Bejan number ( Be ) are obtained to analyze the heat transfer characteristics and irreversibility, respectively. The Rayleigh number and the vertical distance significantly influence the flow and thermal characteristics within the channel. Besides, an artificial neural network (ANN) model is used to predict the distribution of local Nusselt number. The performance of present ANN model is evaluated by comparing the tendency and quantitative values with the direct numerical simulation (DNS) results. The results show that the ANN model used in this study can precisely predict the correlation between the input parameters and output parameter with lesser computational time and cost compared to the DNS.

关键词： Rayleigh-Benard convection Vertical distance Heat transfer performance Irreversibility Artificial neural network supervised learning algorithm

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A Novel supervised learning algorithm for Musical Instrument Classification

A Novel Supervised Learning Algorithm for Musical Instrument...

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35th International Conference on Telecommunications and Signal Processing (TSP)

作者： Rui, Rui Bao, Changchun Beijing Univ Technol Sch Elect Informat & Control Engn Speech & Audio Signal Proc Lab Beijing 100124 Peoples R China

ISBN: (纸本)9781467311182

In this paper, a novel supervised learning algorithm for automatic classification of individual musical instrument sounds is addressed deriving from the idea of supervised non-negative matrix factorization (NMF) algorithm. In our approach, the orthogonal basis matrix could be obtained without updating the matrix iteratively, which supervised NMF algorithm is unable to do. Afterwards, each data is projected onto several training orthogonal basis matrices and three classifiers have been employed to compare the performance with different methods. In addition, feature selection is also applied in order to choose the most discriminative features for instrument classification. The results indicate that the classification accuracy of proposed method is 87.6%, which is comparable to the performance of supervised NMF algorithm for the same experiments.

关键词： Musical instrument classification supervised learning algorithm feature selection

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Machine learning for inferring animal behavior from location and movement data

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ECOLOGICAL INFORMATICS 2019年 49卷 69-76页

作者： Wang, Guiming Mississippi State Univ Dept Wildlife Fisheries & Aquaculture Mail Stop 9690 Mississippi State MS 39762 USA

Movement ecology has rapidly advanced owing to recent developments of animal-attached devices and wide applications of sophisticated statistical and machine learning techniques in analysis of animal movement data. Global Positioning System (GPS) transmitters used for estimating animal locations and tri-axial accelerometers used for measuring the 3-dimensional accelerations of animal's motion aid researchers in collecting location and locomotion data at fine spatial and temporal scales. Machine learning and other advanced statistical methods bridge conceptual models to data, providing insights into ecological and physiological mechanisms underlying animal behavior and movements. This study reviews the general principles and applications of state space models, hidden Markov models, random forests, and support vector machines in the inference of animal behavior from movement data. Unsupervised learning algorithms, including Bayesian state space models implemented for robust correlated random walk models and hidden Markov models, help infer different behavioral modes using GPS location data. State space models can account for measurement error in GPS locations and estimate the true locations of animals;however, without including movement-state switching, state space models do not infer behavioral modes directly. On the contrary, hidden Markov models estimate the probabilities that animals switch between different behavioral modes. Nevertheless, hidden Markov models neither directly estimate animal locations nor account for measurement error explicitly. supervised learning algorithms integrate data on locations and directional accelerations with synchronized behavioral observations (i.e., labels) to classify behaviors to pre-defined behavioral categories. Unlike unsupervised learning, supervised learning requires behavioral observations to label locations and accelerometer data to train the learning algorithms. However, behavioral observations synchronized with relocatio

关键词： Behavioral classification Computational ecology Hidden Markov model Movement ecology State space model supervised learning algorithm Unsupervised learning algorithm

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Stress Evaluation in Simulated Autonomous and Manual Driving through the Analysis of Skin Potential Response and Electrocardiogram Signals

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SENSORS 2020年第9期20卷 2494-2494页

作者： Zontone, Pamela Affanni, Antonio Bernardini, Riccardo Del Linz, Leonida Piras, Alessandro Rinaldo, Roberto Univ Udine Polytech Dept Engn & Architecture Via Sci 206 I-33100 Udine Italy

The evaluation of car drivers' stress condition is gaining interest as research on Autonomous Driving Systems (ADS) progresses. The analysis of the stress response can be used to assess the acceptability of ADS and to compare the driving styles of different autonomous drive algorithms. In this contribution, we present a system based on the analysis of the Electrodermal Activity Skin Potential Response (SPR) signal, aimed to reveal the driver's stress induced by different driving situations. We reduce motion artifacts by processing two SPR signals, recorded from the hands of the subjects, and outputting a single clean SPR signal. Statistical features of signal blocks are sent to a supervised learning algorithm, which classifies between stress and normal driving (non-stress) conditions. We present the results obtained from an experiment using a professional driving simulator, where a group of people is asked to undergo manual and autonomous driving on a highway, facing some unexpected events meant to generate stress. The results of our experiment show that the subjects generally appear more stressed during manual driving, indicating that the autonomous drive can possibly be well received by the public. During autonomous driving, however, significant peaks of the SPR signal are evident during unexpected events. By examining the electrocardiogram signal, the average heart rate is generally higher in the manual case compared to the autonomous case. This further supports our previous findings, even if it may be due, in part, to the physical activity involved in manual driving.

关键词： autonomous driving stress recognition skin potential response electrocardiogram supervised learning algorithm

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Partial discharge pattern recognition using variable predictive model-based class discrimination with kernel partial least squares regression

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2018年第3期12卷 360-367页

作者： Jia, Yafei Zhu, Yongli North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China

The pattern recognition of partial discharge (PD) is critical to evaluate the insulation condition and locate the defect of a power transformer. The existing pattern recognition methods fail to make use of the inter-relations of the extracted features of PD signals. In fact, the inter-relations can show distinct dissimilarities between different classes of the signals. To overcome the defect of existing pattern recognition methods, the variable predictive model-based class discrimination (VPMCD), a new pattern recognition method, is introduced for the pattern recognition of PD in this study. However, the original VPMCD lacks general expression ability of the inter-relations of extracted features and inherits the shortcomings of least squares (LS) regression. To overcome the above defects, an improved VPMCD based on kernel partial LS (KPLS) regression, which is called as KPLS-VPMCD, is proposed in this study. Experiments and analyses are implemented using both UCI datasets and the extracted features of PD signals. The experiments show that the performance of the proposed KPLS-VPMCD is better than those of the existing methods such as VPMCD, back propagation neural networks, and support vector machines. The conclusion is that KPLS-VPMCD is an efficient supervised learning algorithm with consistency and good performance for PD pattern recognition.

关键词： partial discharge measurement pattern recognition prediction theory least squares approximations regression analysis power transformer insulation feature extraction partial discharge pattern recognition method variable predictive model-based class discrimination PD pattern recognition method insulation condition evaluation power transformer feature extraction LS regression improved VPMCD kernel partial least square regression KPLS regression UCI dataset intelligent patter recognition method backpropagation neural network support vector machine supervised learning algorithm

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Keyphrase Extraction Based on Prior Knowledge 18

Keyphrase Extraction Based on Prior Knowledge

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18th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL)

作者： He, Guoxiu Fang, Junwei Cui, Haoran Wu, Chuan Lu, Wei Wuhan Univ Sch Informat Management Wuhan Hubei Peoples R China

ISBN: (纸本)9781450351782

Keyphrase is an important way to quickly get the topic of a document by providing highly-summative information. The previous approaches for keyphrase extraction simply rank keyphrases according to statistics-based model or graph-based model, which ignore the influence of external knowledge. In this paper, we take prior knowledge, which contains controlled vocabulary of keyphrases and their prior probability, into consideration to enhance previous methods. First, we build a controlled vocabulary of keyphrases introduced by keyphrases from existing collections and a keyphrase candidate set is filtered from a given document by it. Then, we use prior probability to represent the importance of keyphrases candidate with TF-IDF and TextRank. Finally, a supervised learning algorithm is used to learn optimal weights of these three features. Experiments on four benchmark datasets show the great advantages of prior knowledge on keyphrase extraction. Furthermore, we achieve competitive performance compared with the state-of-the art methods.

关键词： Keyphrase Extraction Prior Knowledge TF-IDF TextRank supervised learning algorithm

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Two-step Prediction Technique for Dengue Outbreak in Thailand 6

Two-step Prediction Technique for Dengue Outbreak in Thailan...

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6th International Electrical Engineering Congress (iEECON)

作者： Nakvisut, Amomsak Phienthrakul, Tanasanee Mahidol Univ Dept Comp Engn Fac Engn Nakhon Pathom Thailand

ISBN: (纸本)9781538623176

Dengue fever is a tropical disease caused by dengue virus. This virus is transmitted by mosquitoes. Dengue can also be transmitted via infected blood products and through organ donation. There are many infected people in each year. Thus, if the number of patients can be predicted, vaccines for dengue fever will be able to prepare for serving all patients in that area. This research proposed a two-step prediction method that combines time series forecasting analysis and supervised learning techniques to predict the number of dengue fever patient cases. In generally, the result of prediction from only the number of patient cases is not good enough for application;accuracy and confidence are low. In this research, the environmental factors are considered and predicted. Then, these factors are used for predicting the number patient cases. The experimental results show that the proposed two-step prediction technique is a good choice for dengue fever prediction. The accuracy is better than the simple prediction technique.

关键词： Dengue Time series analysis supervised learning algorithm Two-step prediction

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