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Segmentation-based approach for trend analysis and structural breaks in rainfall time series (1851-2006) over India

HYDROLOGICAL SCIENCES JOURNAL 2020年第9期65卷 1583-1595页

作者： Priyadarshi, Niraj Bandyopadhyay, Soumya Chowdary, V. M. Chandrasekar, K. Chockalingam, Jeganathan Raj, Uday Jha, Chandra Shekhar NRSC ISRO Reg Remote Sensing Ctr East Kolkata India NRSC ISRO Reg Remote Sensing Ctr North New Delhi India Indian Space Res Org ISRO Natl Remote Sensing Ctr NRSC Hyderabad India Birla Inst Technol Dept Remote Sensing Mesra Bit Mesra Ranchi India

This study analysed long-term rainfall data (1851-2006) over seven climatic zones of India at seasonal and annual scales based on three techniques: (i) linear regression, (ii) multifractal detrended fluctuation analysis (MFDFA) and (iii) bayesian algorithm. The linear regression technique was used for trend analysis of short-term (30 years) and long-term (156 years) rainfall data. The MFDFA revealed small- and large-scale fluctuations, whereas the bayesian algorithm helped in quantifying the uncertainty in break-point detection from the rainfall time series. Major break points years identified through bayesian algorithm were 1888, 1904 and 1976. The MFDFA technique identified that high fluctuation years were between 1871-1890, 1891-1910 and 1951-1970. Linear regression-based analysis revealed 1881-1910 and 1971-2006 as break-point periods in the North Mountainous Indian region. A similar analysis was carried out for India as a whole, as well as its seven climatic zones.

关键词： time series rainfall linear regression detrended fluctuation analysis bayesian algorithm

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Particle Source Localization With a Low-Cost Robotic Sensor System: algorithmic Design and Performance Evaluation

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IEEE SENSORS JOURNAL 2020年第21期20卷 13074-13085页

作者： Chen, Xinxing Marjovi, Ali Huang, Jian Martinoli, Alcherio Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Ecole Polytech Fed Lausanne EPFL Sch Architecture Civil & Environm Engn Distributed Intelligent Syst & Algorithms Lab DIS CH-1015 Lausanne Switzerland

In this paper we tackle the problem of finding the source of particulate matter with a mobile robot equipped with a low-cost multi-channel optical particle counting sensor. The proposed method is based on the Infotaxis odor source localization algorithm and makes multiple modifications to adapt it to particle plumes. In particular, we propose three simple but efficient ways to fuse multiple probability maps associated with various particle sizes and use the resulted integrated map to guide the robot's movements. A refined measurement data collection is conducted in a wind tunnel to fit the particle plume model. The method with three proposed integration strategies is evaluated in simulation and in the wind tunnel emulating realistic environmental conditions in a repeatable fashion. In particular, we have investigated the impact of two environmental parameters - the wind speed and source release rate on the algorithm performance. The proposed algorithm with the weighted multi-modality map integration strategy outperforms the original Infotaxis and the other two variants. In high wind speed, the proposed algorithm is able on average to estimate the source location with less than 1 meter error in the 80 m(2) wind tunnel arena.

关键词： particle source localization low-cost optical particle counter mobile robotics bayesian algorithm wind tunnel experiments

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An approach to predict the isobaric specific heat capacity of nitrides/ethylene glycol-based nanofluids using support vector regression

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JOURNAL OF ENERGY STORAGE 2020年 29卷

作者： Alade, Ibrahim Olanrewaju Abd Rahman, Mohd Amiruddin Saleh, Tawfik A. Univ Putra Malaysia Dept Phys Fac Sci Upm Serdang 43400 Malaysia King Fahd Univ Petr & Minerals Dept Chem Dhahran 31261 Saudi Arabia

This study presents a novel strategy based on bayesian support vector regression for the estimation of the specific heat capacity of nitrides/ethylene glycol-based nanofluid. The nanoparticles considered are aluminium nitride (AlN), silicon nitride (Si3N4) and titanium nitride (TiN). The proposed model was built using simple and easy-toobtain inputs such as the size of the nanoparticles (20, 30, 50, and 80 nm), the molar mass of the nanoparticles, mass fraction of nanoparticles (0.01 - 0.1) and the temperature (288.15 K, 298.15 K, and 308.15 K). Our suggested model showed better prediction accuracy over the analytical models for the estimation of specific heat capacity of nitrides/ethylene glycol nanofluids. Given the simplicity of the model inputs and the accuracy of the model, the approach presented provides a more reliable prediction of specific heat capacity of nitrides-ethylene glycol-based nanofluids than previous models.

关键词： Ethylene-glycol Nitrides Nanoparticles Nanofluid Support vector regression bayesian algorithm

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Application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide

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SOLAR ENERGY 2020年第0期197卷 485-490页

作者： Alade, Ibrahim Olanrewaju Abd Rahman, Mohd Amiruddin Abbas, Zulkifly Yaakob, Yazid Saleh, Tawfik A. Univ Putra Malaysia Fac Sci Dept Phys Upm Serdang 43400 Malaysia King Fahd Univ Petr & Minerals KFUPM Business Sch Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Dept Chem Dhahran 31261 Saudi Arabia

This paper presents the modelling of the specific heat capacity (SHC) of CuO/water nanofluids using a support vector regression (SVR) and artificial neural network models (ANN). The models presented were developed from the experimental data of SCH of CuO nanoparticles, the volume fractions of CuO nanoparticles and fluid temperature. The volume fraction of CuO nanoparticles considered ranges from 0.4 to 2% while the temperature range includes 293-338 K. The results obtained revealed that the SVR model exhibits slightly higher accuracy compared to the ANN model. However, both the SVR and ANN models clearly demonstrate better prediction performance for the SHC of CuO/water nanofluids compared to the existing theoretical models. The results obtained in this study proves that machine learning models provide a more accurate estimation of SHC of CuO/water nanofluids and they are recommended for heat transfer calculations due to their superior accuracy.

关键词： Nanofluids Specific heat capacity Support vector regression Artificial neural network bayesian algorithm

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Machine Learning-Based Identification Strategy of Fuel Surrogates for the CFD Simulation of Stratified Operations in Low Temperature Combustion Modes

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ENERGIES 2021年第15期14卷 4623页

作者： Mariani, Valerio Pulga, Leonardo Bianchi, Gian Marco Falfari, Stefania Forte, Claudio Univ Bologna Dept Ind Engn DIN Alma Mater Studiorum Viale Risorgimento 2 I-40136 Bologna Italy NAIS Srl Via M Callas 4 I-40131 Bologna Italy

Many researchers in industry and academia are showing an increasing interest in the definition of fuel surrogates for Computational Fluid Dynamics simulation applications. This need is mainly driven by the necessity of the engine research community to anticipate the effects of new gasoline formulations and combustion modes (e.g., Homogeneous Charge Compression Ignition, Spark Assisted Compression Ignition) to meet future emission regulations. Since those solutions strongly rely on the tailored mixture distribution, the simulation and accurate prediction of the mixture formation will be mandatory. Focusing purely on the definition of surrogates to emulate liquid phase and liquid-vapor equilibrium of gasolines, the following target properties are considered in this work: density, Reid vapor pressure, chemical macro-composition and volatility. A set of robust algorithms has been developed for the prediction of volatility and Reid vapor pressure. A bayesian optimization algorithm based on a customized merit function has been developed to allow for the efficient definition of surrogate formulations from a palette of 15 pure compounds. The developed methodology has been applied on different real gasolines from literature in order to identify their optima surrogates. Furthermore, the 'unicity' of the surrogate composition is discussed by comparing the optimum solution with the most different one available in the pool of equivalent-valuable solutions. The proposed methodology has proven the potential to formulate surrogates characterized by an overall good agreement with the target properties of the experimental gasolines (max relative error below 10%, average relative error around 3%). In particular, the shape and the end-tails of the distillation curve are well captured. Furthermore, an accurate prediction of key chemical macro-components such as ethanol and aromatics and their influence on evaporative behavior is achieved. The study of the 'unicity' of the surrogate comp

关键词： stratified fuel surrogate fuel gasoline surrogate bayesian algorithm machine learning

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High precision indoor positioning algorithm of single LED lamp based on A-Bayes

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OPTIK 2021年 241卷 167190-167190页

作者： Qin, Ling Niu, Ben Li, Bao-Shan Hu, Xiao-Li Du, Yong-Xing Inner Mongolia Univ Sci & Technol Sch Informat Engn Baotou 014010 Inner Mongolia Peoples R China

With the rapid development of information technology, the application field of city positioning technology is more and more extensive. Traditional indoor positioning technology has shortcomings in positioning accuracy, equipment cost, security and so on. Visible light indoor positioning technology can effectively solve the above problems. This paper presents an Adaptive boosting- Bayes (A-Bayes) localization algorithm. In this algorithm, three tilted photodetectors and one horizontal PD are used as receivers to measure the received optical power of each PD and set up a fingerprint database. The Adaboost strong classifier is used to divide the test space into two regions: the center and the edge. In the online positioning stage, for the measured received optical power, the region to which it belongs is first determined, and then the bayesian matching algorithm is used to match the regions to complete the positioning. The experimental results show that in the test space of, and the overall average positioning error is 9.27 cm. Compared with the traditional indoor positioning algorithm, the positioning error of the proposed algorithm is smaller;Compared with the traditional multi lamp location algorithm, the algorithm proposed in this paper avoids the interference between multi light source symbols, and the equipment is simpler;Compared with the traditional fingerprint matching algorithm, the proposed algorithm has faster positioning speed.

关键词： Visible light indoor positioning Received optical power bayesian algorithm AdaBoost strong classifier

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Evaluation algorithm of High Voltage Switchgear Based on bayesian Fusion

Evaluation Algorithm of High Voltage Switchgear Based on Bay...

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International Conference on Power System Technology (POWERCON)

作者： Xie, Jing Shu, Hongchun Zhang, Wenying Song, Qi Luo, Lingl Kunming Univ Sci & Technol Power Syst Kunming Yunnan Peoples R China

ISBN: (纸本)9781538664612

The failure rate of high voltage switchgear equipment is always high. In recent years, the problem of high failure rate of equipment has become more prominent due to the upgrading of old equipment. At the same time, the management of high-voltage switchgear and the forecast analysis of state trend are also important components of intelligent substation. For the state evaluation of electric equipment, there are diversity, randomness and fuzzy characteristics in fault phenomenon and failure mechanism. Based on the current assessment concept of "trend is heavier than the threshold", the algorithm that can reflect the variation trend of equipment failure and reflect the fault uncertainty is the main algorithm in the evaluation strategy. This paper analyzes the applicability of the three evaluation algorithms of fuzzy hierarchical theory, catastrophe theory and fuzzy cluster theory. Three of these algorithms adopt the fuzzy theory, to be able to satisfy the multidimensional input and the equipment under the state comprehensive evaluation, through fuzzy vector reflected device status interval and the trend of development, can adapt to the actual working conditions that some state information is missing. However, the characteristics of each algorithm inevitably have their own limitations, and the evaluation results will be different. To this end, this paper introduces information fusion concept, established comprehensive state evaluation model of switchgear based on the bayesian decision-level theory according to the characteristics of the different assessment methods and its applicability. It proposed bayesian fusion algorithm on decision layer based on the similarity. The validity of the algorithm is verified by the actual field data. The fusion algorithm can effectively improve the accuracy and reliability of the evaluation results, and enhance the fault tolerance performance of the evaluation system to achieve the function complementation between different sub-algorith

关键词： bayesian algorithm State Evaluation decision - level information fusion High Voltage Switchgear

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Design of New Media Data Processing System

Design of New Media Data Processing System

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International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI)

作者： Shan, Hui Zhang, Hongshen Mi, Xueming Dalian Univ Sci & Technol Party Comm Off Dalian Peoples R China

ISBN: (纸本)9781665423168

This paper is mainly for the design of new media data processing system. The research of new media data processing shows that the traditional database processing new media data is more and more huge, which causes the operating system to do a lot of output and input operations (I / O) when querying, and the processing speed is slow. Through the analysis, the main reason that affects the new media data processing is the large amount of data. In this paper, bayesian algorithm classifier model is used, and Map/Reduce function is used in the parallel computing framework. Multi computing resource nodes are used for parallel processing to effectively improve the speed of new media data processing.

关键词： new media data Map/Reduce function bayesian algorithm parallel computing framework

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Micromechanical model of nanoparticle compaction and shock waves in metal powders

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INTERNATIONAL JOURNAL OF PLASTICITY 2021年 147卷 103102-103102页

作者： Mayer, Alexander E. Chelyabinsk State Univ Bratiev Kashirinykh St 129 Chelyabinsk 454001 Russia South Ural State Univ Lenin Prospect 76 Chelyabinsk 454080 Russia

A micromechanical model of metal powder compaction is developed as a generalization of the previous model proposed by Mayer et al. (2020). The model is applicable for an arbitrary axisymmetric deformed state including the uniaxial compression and the uniform tri-axial one. It describes the stage of compression of initially spherical particles, the percolation transition to the system of isolated pores and the stage of the collapse of pores. At the first stage, the metal particles are approximated by spheres with indented sectors with two independent indentation depths: One along the dedicated axis and another one along the perpendicular directions. The model of porous medium is formulated in finite deformations and the dislocation-driven plasticity model (Krasnikov and Mayer, 2015) is used to describe the pore collapse at the last stage. MD simulations of uniform tri-axial and uniaxial compression of iron, copper and magnesium nanoparticles of 6, 12 and 18 nanometers in diameter are performed for a number of temperatures. Activity of dislocations, which form dense structures, is the main driver of the plastic deformation of nanoparticles during compaction, while the presence of twins is revealed for copper and magnesium. MD data is used for identification of the model parameters and verification of the model. bayesian algorithm is implemented for parameter identification, which reveals itself as an efficient tool and allows us to fit the model with the same set of parameters to MD data for all investigated temperatures, particle diameters and modes of compression. The developed and parameterized micromechanical model is embedded into the continuum model of shock wave propagation in metal powder as a constitutive model of the substance. Both partial and complete compaction by the shock wave is investigated by an example of iron. The presence of elastic precursor is revealed for the case of nanopowders because of high yield strength of nanoparticle material achieved d

关键词： Nanoparticles Metal powder Compaction Micromechanical model Molecular dynamics bayesian algorithm Shock waves Plate impact Elastic precursor

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A Study on Sentiment Analysis Techniques of Twitter Data

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第2期10卷 361-374页

作者： Alsaeedi, Abdullah Khan, Mohammad Zubair Taibah Univ Dept Comp Sci Coll Comp Sci & Engn Madinah Saudi Arabia

The entire world is transforming quickly under the present innovations. The Internet has become a basic requirement for everybody with the Web being utilized in every field. With the rapid increase in social network applications, people are using these platforms to voice them their opinions with regard to daily issues. Gathering and analyzing peoples' reactions toward buying a product, public services, and so on are vital. Sentiment analysis (or opinion mining) is a common dialogue preparing task that aims to discover the sentiments behind opinions in texts on varying subjects. In recent years, researchers in the field of sentiment analysis have been concerned with analyzing opinions on different topics such as movies, commercial products, and daily societal issues. Twitter is an enormously popular microblog on which clients may voice their opinions. Opinion investigation of Twitter data is a field that has been given much attention over the last decade and involves dissecting "tweets" (comments) and the content of these expressions. As such, this paper explores the various sentiment analysis applied to Twitter data and their outcomes.

关键词： Twitter sentiment Web data text mining SVM bayesian algorithm hybrid ensembles

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